Feature/ontology class clean (#249)

* [add] Complete ontology engineering feature implementation

* [add] Add ontology feature integration and validation utilities

* [add] Add OWL validator and validation utilities

* [fix] Add missing render_ontology_extraction_prompt function

* [fix]Add dependencies, fix functionality

api/app/controllers/__init__.py

@@ -45,6 +45,7 @@ from . import (
     home_page_controller,
     memory_perceptual_controller,
     memory_working_controller,
+    ontology_controller,
 )
 
 # 创建管理端 API 路由器
@@ -90,5 +91,6 @@ manager_router.include_router(implicit_memory_controller.router)
 manager_router.include_router(memory_perceptual_controller.router)
 manager_router.include_router(memory_working_controller.router)
 manager_router.include_router(file_storage_controller.router)
+manager_router.include_router(ontology_controller.router)
 
 __all__ = ["manager_router"]

api/app/controllers/ontology_controller.py

@@ -0,0 +1,964 @@
+"""本体提取API控制器
+
+本模块提供本体提取系统的RESTful API端点。
+
+Endpoints:
+    POST /api/memory/ontology/extract - 提取本体类
+    POST /api/memory/ontology/export - 导出OWL文件
+    POST /api/memory/ontology/scene - 创建本体场景
+    PUT /api/memory/ontology/scene/{scene_id} - 更新本体场景
+    DELETE /api/memory/ontology/scene/{scene_id} - 删除本体场景
+    GET /api/memory/ontology/scene/{scene_id} - 获取单个场景
+    GET /api/memory/ontology/scenes - 获取场景列表
+    POST /api/memory/ontology/class - 创建本体类型
+    PUT /api/memory/ontology/class/{class_id} - 更新本体类型
+    DELETE /api/memory/ontology/class/{class_id} - 删除本体类型
+    GET /api/memory/ontology/class/{class_id} - 获取单个类型
+    GET /api/memory/ontology/classes - 获取类型列表
+"""
+
+import logging
+import tempfile
+from typing import Dict, Optional
+
+from fastapi import APIRouter, Depends, HTTPException, Header
+from sqlalchemy.orm import Session
+
+from app.core.error_codes import BizCode
+from app.core.logging_config import get_api_logger
+from app.core.response_utils import fail, success
+from app.db import get_db
+from app.dependencies import get_current_user
+from app.models.user_model import User
+from app.services.memory_base_service import Translation_English
+from app.core.memory.models.ontology_models import OntologyClass
+from typing import List
+from app.schemas.ontology_schemas import (
+    ExportRequest,
+    ExportResponse,
+    ExtractionRequest,
+    ExtractionResponse,
+    SceneCreateRequest,
+    SceneUpdateRequest,
+    SceneResponse,
+    SceneListResponse,
+    ClassCreateRequest,
+    ClassUpdateRequest,
+    ClassResponse,
+    ClassListResponse,
+)
+from app.schemas.response_schema import ApiResponse
+from app.services.ontology_service import OntologyService
+from app.core.memory.llm_tools.openai_client import OpenAIClient
+from app.core.memory.utils.validation.owl_validator import OWLValidator
+from app.services.model_service import ModelConfigService
+
+
+api_logger = get_api_logger()
+logger = logging.getLogger(__name__)
+
+router = APIRouter(
+    prefix="/memory/ontology",
+    tags=["Ontology"],
+)
+
+
+async def translate_ontology_classes(
+    classes: List[OntologyClass], 
+    model_id: str
+) -> List[OntologyClass]:
+    """翻译本体类列表
+    
+    将本体类的中文字段翻译为英文，包括：
+    - name_chinese: 中文名称
+    - description: 描述
+    - examples: 示例列表
+    
+    Args:
+        classes: 本体类列表
+        model_id: LLM模型ID，用于翻译
+        
+    Returns:
+        List[OntologyClass]: 翻译后的本体类列表
+    """
+    translated_classes = []
+    
+    for ontology_class in classes:
+        # 创建类的副本，避免修改原对象
+        translated_class = ontology_class.model_copy(deep=True)
+        
+        # 翻译 name_chinese 字段
+        if translated_class.name_chinese:
+            try:
+                translated_class.name_chinese = await Translation_English(
+                    model_id, 
+                    translated_class.name_chinese
+                )
+            except Exception as e:
+                logger.warning(f"Failed to translate name_chinese: {e}")
+                # 保留原文
+        
+        # 翻译 description 字段
+        if translated_class.description:
+            try:
+                translated_class.description = await Translation_English(
+                    model_id, 
+                    translated_class.description
+                )
+            except Exception as e:
+                logger.warning(f"Failed to translate description: {e}")
+                # 保留原文
+        
+        # 翻译 examples 列表
+        if translated_class.examples:
+            translated_examples = []
+            for example in translated_class.examples:
+                try:
+                    translated_example = await Translation_English(
+                        model_id, 
+                        example
+                    )
+                    translated_examples.append(translated_example)
+                except Exception as e:
+                    logger.warning(f"Failed to translate example: {e}")
+                    translated_examples.append(example)  # 保留原文
+            translated_class.examples = translated_examples
+        
+        translated_classes.append(translated_class)
+    
+    return translated_classes
+
+
+def _get_ontology_service(
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user),
+    llm_id: str = None
+) -> OntologyService:
+    """获取OntologyService实例的依赖注入函数
+    
+    指定的llm_id获取LLM配置,创建OpenAIClient和OntologyService实例。
+    
+    Args:
+        db: 数据库会话
+        current_user: 当前用户
+        llm_id: 可选的LLM模型ID,如果提供则使用指定模型,否则使用工作空间默认模型
+        
+    Returns:
+        OntologyService: 本体提取服务实例
+        
+    Raises:
+        HTTPException: 如果无法获取LLM配置
+    """
+    try:
+        import uuid
+        
+        # 必须提供llm_id
+        if not llm_id:
+            logger.error(f"llm_id is required but not provided - user: {current_user.id}")
+            raise HTTPException(
+                status_code=400,
+                detail="必须提供llm_id参数"
+            )
+        
+        logger.info(f"Using specified LLM model: {llm_id}")
+        
+        # 验证llm_id格式
+        try:
+            model_id = uuid.UUID(llm_id)
+        except ValueError:
+            logger.error(f"Invalid llm_id format: {llm_id}")
+            raise HTTPException(
+                status_code=400,
+                detail="无效的LLM模型ID格式"
+            )
+        
+        # 获取指定的模型配置
+        try:
+            model_config = ModelConfigService.get_model_by_id(db=db, model_id=model_id)
+        except Exception as e:
+            logger.error(f"Model {llm_id} not found: {str(e)}")
+            raise HTTPException(
+                status_code=400,
+                detail=f"找不到指定的LLM模型: {llm_id}"
+            )
+        
+        # 检查是否为组合模型
+        if hasattr(model_config, 'is_composite') and model_config.is_composite:
+            logger.error(f"Model {llm_id} is a composite model, which is not supported for ontology extraction")
+            raise HTTPException(
+                status_code=400,
+                detail="本体提取不支持使用组合模型，请选择单个模型"
+            )
+        
+        # 验证模型配置了API密钥
+        if not model_config.api_keys:
+            logger.error(f"Model {llm_id} has no API key configuration")
+            raise HTTPException(
+                status_code=400,
+                detail="指定的LLM模型没有配置API密钥"
+            )
+        
+        api_key_config = model_config.api_keys[0]
+        
+        logger.info(
+            f"Using specified model - user: {current_user.id}, "
+            f"model_id: {llm_id}, model_name: {api_key_config.model_name}"
+        )
+        
+        # 创建模型配置对象
+        from app.core.models.base import RedBearModelConfig
+        
+        llm_model_config = RedBearModelConfig(
+            model_name=api_key_config.model_name,
+            provider=model_config.provider if hasattr(model_config, 'provider') else "openai",
+            api_key=api_key_config.api_key,
+            base_url=api_key_config.api_base,
+            max_retries=3,
+            timeout=60.0
+        )
+        
+        # 创建OpenAI客户端
+        llm_client = OpenAIClient(model_config=llm_model_config)
+        
+        # 创建OntologyService
+        service = OntologyService(llm_client=llm_client, db=db)
+        
+        logger.debug(
+            f"OntologyService created successfully - "
+            f"user: {current_user.id}, model: {api_key_config.model_name}"
+        )
+        
+        return service
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Failed to create OntologyService: {str(e)}", exc_info=True)
+        raise HTTPException(
+            status_code=500,
+            detail=f"创建本体提取服务失败: {str(e)}"
+        )
+
+
+@router.post("/extract", response_model=ApiResponse)
+async def extract_ontology(
+    request: ExtractionRequest,
+    language_type: str = Header(default="zh", alias="X-Language-Type"),
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
+):
+    """提取本体类
+    
+    从场景描述中提取符合OWL规范的本体类。
+    提取结果仅返回给前端，不会自动保存到数据库。
+    前端可以从返回结果中选择需要的类型，然后调用 /class 接口创建类型。
+    支持中英文切换，通过 X-Language-Type Header 指定语言。
+    
+    Args:
+        request: 提取请求,包含scenario、domain、llm_id和scene_id
+        language_type: 语言类型，'zh'（中文）或 'en'（英文），默认 'zh'
+        db: 数据库会话
+        current_user: 当前用户
+        
+    Returns:
+        ApiResponse: 包含提取结果的响应
+        
+    Response format:
+        {
+            "code": 200,
+            "msg": "本体提取成功",
+            "data": {
+                "classes": [
+                    {
+                        "id": "147d9db50b524a9e909e01a753d3acdd",
+                        "name": "Patient",
+                        "name_chinese": "患者",
+                        "description": "在医疗机构中接受诊疗、护理或健康管理的个体",
+                        "examples": ["糖尿病患者", "术后康复患者", "门诊初诊患者"],
+                        "parent_class": null,
+                        "entity_type": "Person",
+                        "domain": "Healthcare"
+                    },
+                    ...
+                ],
+                "domain": "Healthcare",
+                "extracted_count": 7
+            }
+        }
+    """
+    api_logger.info(
+        f"Ontology extraction requested by user {current_user.id}, "
+        f"scenario_length={len(request.scenario)}, "
+        f"domain={request.domain}, "
+        f"llm_id={request.llm_id}, "
+        f"scene_id={request.scene_id}, "
+        f"language_type={language_type}"
+    )
+    
+    try:
+        # 获取当前工作空间ID
+        workspace_id = current_user.current_workspace_id
+        if not workspace_id:
+            api_logger.warning(f"User {current_user.id} has no current workspace")
+            return fail(BizCode.BAD_REQUEST, "请求参数无效", "当前用户没有工作空间")
+        
+        # 创建OntologyService实例,传入llm_id
+        service = _get_ontology_service(
+            db=db,
+            current_user=current_user,
+            llm_id=request.llm_id
+        )
+        
+        # 调用服务层执行提取，传入scene_id和workspace_id
+        result = await service.extract_ontology(
+            scenario=request.scenario,
+            domain=request.domain,
+            scene_id=request.scene_id,
+            workspace_id=workspace_id
+        )
+        
+        # ===== 新增：翻译逻辑 =====
+        # 如果需要英文，则翻译数据
+        if language_type != 'zh':
+            api_logger.info(f"Translating extraction result to English")
+            
+            # 翻译 classes 列表
+            result.classes = await translate_ontology_classes(
+                result.classes, 
+                request.llm_id
+            )
+            
+            # 翻译 domain 字段
+            if result.domain:
+                try:
+                    result.domain = await Translation_English(
+                        request.llm_id, 
+                        result.domain
+                    )
+                except Exception as e:
+                    logger.warning(f"Failed to translate domain: {e}")
+                    # 保留原文
+        # ===== 翻译逻辑结束 =====
+        
+        # 构建响应
+        response = ExtractionResponse(
+            classes=result.classes,
+            domain=result.domain,
+            extracted_count=len(result.classes)
+        )
+        
+        api_logger.info(
+            f"Ontology extraction completed, extracted {len(result.classes)} classes, "
+            f"saved to scene {request.scene_id}, language={language_type}"
+        )
+        
+        return success(data=response.model_dump(), msg="本体提取成功")
+        
+    except ValueError as e:
+        # 验证错误 (400)
+        api_logger.warning(f"Validation error in extraction: {str(e)}")
+        return fail(BizCode.BAD_REQUEST, "请求参数无效", str(e))
+        
+    except RuntimeError as e:
+        # 运行时错误 (500)
+        api_logger.error(f"Runtime error in extraction: {str(e)}", exc_info=True)
+        return fail(BizCode.INTERNAL_ERROR, "本体提取失败", str(e))
+        
+    except Exception as e:
+        # 未知错误 (500)
+        api_logger.error(f"Unexpected error in extraction: {str(e)}", exc_info=True)
+        return fail(BizCode.INTERNAL_ERROR, "本体提取失败", str(e))
+
+
+@router.post("/export", response_model=ApiResponse)
+async def export_owl(
+    request: ExportRequest,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
+):
+    """导出OWL文件
+    
+    将提取的本体类导出为OWL文件,支持多种格式。
+    导出操作不需要LLM,只使用OWL验证器和Owlready2库。
+    
+    Args:
+        request: 导出请求,包含classes、format和include_metadata
+        db: 数据库会话
+        current_user: 当前用户
+        
+    Returns:
+        ApiResponse: 包含OWL文件内容的响应
+        
+    Supported formats:
+        - rdfxml: 标准OWL RDF/XML格式(完整)
+        - turtle: Turtle格式(可读性好)
+        - ntriples: N-Triples格式(简单)
+        - json: JSON格式(简化,只包含类信息)
+        
+    Response format:
+        {
+            "code": 200,
+            "msg": "OWL文件导出成功",
+            "data": {
+                "owl_content": "...",
+                "format": "rdfxml",
+                "classes_count": 7
+            }
+        }
+    """
+    api_logger.info(
+        f"OWL export requested by user {current_user.id}, "
+        f"classes_count={len(request.classes)}, "
+        f"format={request.format}, "
+        f"include_metadata={request.include_metadata}"
+    )
+    
+    try:
+        # 验证格式
+        valid_formats = ["rdfxml", "turtle", "ntriples", "json"]
+        if request.format not in valid_formats:
+            api_logger.warning(f"Invalid export format: {request.format}")
+            return fail(
+                BizCode.BAD_REQUEST,
+                "不支持的导出格式",
+                f"format必须是以下之一: {', '.join(valid_formats)}"
+            )
+        
+        # JSON格式直接导出,不需要OWL验证
+        if request.format == "json":
+            owl_validator = OWLValidator()
+            owl_content = owl_validator.export_to_owl(
+                world=None,
+                format="json",
+                classes=request.classes
+            )
+            
+            response = ExportResponse(
+                owl_content=owl_content,
+                format=request.format,
+                classes_count=len(request.classes)
+            )
+            
+            api_logger.info(
+                f"JSON export completed, content_length={len(owl_content)}"
+            )
+            
+            return success(data=response.model_dump(), msg="OWL文件导出成功")
+        
+        # 创建临时文件路径
+        with tempfile.NamedTemporaryFile(
+            mode='w',
+            suffix='.owl',
+            delete=False
+        ) as tmp_file:
+            output_path = tmp_file.name
+        
+        # 导出操作不需要LLM,直接使用OWL验证器
+        owl_validator = OWLValidator()
+        
+        # 验证本体类
+        logger.debug("Validating ontology classes")
+        is_valid, errors, world = owl_validator.validate_ontology_classes(
+            classes=request.classes,
+        )
+        
+        if not is_valid:
+            logger.warning(
+                f"OWL validation found {len(errors)} issues during export: {errors}"
+            )
+            # 继续导出,但记录警告
+        
+        if not world:
+            error_msg = "Failed to create OWL world for export"
+            logger.error(error_msg)
+            return fail(BizCode.INTERNAL_ERROR, "创建OWL世界失败", error_msg)
+        
+        # 导出OWL文件
+        logger.info(f"Exporting to {request.format} format")
+        owl_content = owl_validator.export_to_owl(
+            world=world,
+            output_path=output_path,
+            format=request.format,
+            classes=request.classes
+        )
+        
+        # 构建响应
+        response = ExportResponse(
+            owl_content=owl_content,
+            format=request.format,
+            classes_count=len(request.classes)
+        )
+        
+        api_logger.info(
+            f"OWL export completed, format={request.format}, "
+            f"content_length={len(owl_content)}"
+        )
+        
+        return success(data=response.model_dump(), msg="OWL文件导出成功")
+        
+    except ValueError as e:
+        # 验证错误 (400)
+        api_logger.warning(f"Validation error in export: {str(e)}")
+        return fail(BizCode.BAD_REQUEST, "请求参数无效", str(e))
+        
+    except RuntimeError as e:
+        # 运行时错误 (500)
+        api_logger.error(f"Runtime error in export: {str(e)}", exc_info=True)
+        return fail(BizCode.INTERNAL_ERROR, "OWL文件导出失败", str(e))
+        
+    except Exception as e:
+        # 未知错误 (500)
+        api_logger.error(f"Unexpected error in export: {str(e)}", exc_info=True)
+        return fail(BizCode.INTERNAL_ERROR, "OWL文件导出失败", str(e))
+
+
+# ==================== 本体场景管理接口 ====================
+
+@router.post("/scene", response_model=ApiResponse)
+async def create_scene(
+    request: SceneCreateRequest,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
+):
+    """创建本体场景
+    
+    在当前工作空间下创建新的本体场景。
+    
+    Args:
+        request: 场景创建请求
+        db: 数据库会话
+        current_user: 当前用户
+        
+    Returns:
+        ApiResponse: 包含创建的场景信息
+    """
+    api_logger.info(
+        f"Scene creation requested by user {current_user.id}, "
+        f"name={request.scene_name}"
+    )
+    
+    try:
+        # 获取当前工作空间ID
+        workspace_id = current_user.current_workspace_id
+        if not workspace_id:
+            api_logger.warning(f"User {current_user.id} has no current workspace")
+            return fail(BizCode.BAD_REQUEST, "请求参数无效", "当前用户没有工作空间")
+        
+        # 创建OntologyService实例（不需要LLM）
+        from app.core.memory.llm_tools.openai_client import OpenAIClient
+        from app.core.models.base import RedBearModelConfig
+        
+        # 创建一个空的LLM配置（场景管理不需要LLM）
+        dummy_config = RedBearModelConfig(
+            model_name="dummy",
+            provider="openai",
+            api_key="dummy",
+            base_url="https://api.openai.com/v1"
+        )
+        llm_client = OpenAIClient(model_config=dummy_config)
+        service = OntologyService(llm_client=llm_client, db=db)
+        
+        # 调用服务层创建场景
+        scene = service.create_scene(
+            scene_name=request.scene_name,
+            scene_description=request.scene_description,
+            workspace_id=workspace_id
+        )
+        
+        # 构建响应
+        # 动态计算 type_num
+        type_num = len(scene.classes) if scene.classes else 0
+        
+        response = SceneResponse(
+            scene_id=scene.scene_id,
+            scene_name=scene.scene_name,
+            scene_description=scene.scene_description,
+            type_num=type_num,
+            workspace_id=scene.workspace_id,
+            created_at=scene.created_at,
+            updated_at=scene.updated_at,
+            classes_count=type_num
+        )
+        
+        api_logger.info(f"Scene created successfully: {scene.scene_id}")
+        
+        return success(data=response.model_dump(), msg="场景创建成功")
+        
+    except ValueError as e:
+        api_logger.warning(f"Validation error in scene creation: {str(e)}")
+        return fail(BizCode.BAD_REQUEST, "请求参数无效", str(e))
+        
+    except RuntimeError as e:
+        api_logger.error(f"Runtime error in scene creation: {str(e)}", exc_info=True)
+        return fail(BizCode.INTERNAL_ERROR, "场景创建失败", str(e))
+        
+    except Exception as e:
+        api_logger.error(f"Unexpected error in scene creation: {str(e)}", exc_info=True)
+        return fail(BizCode.INTERNAL_ERROR, "场景创建失败", str(e))
+
+
+@router.put("/scene/{scene_id}", response_model=ApiResponse)
+async def update_scene(
+    scene_id: str,
+    request: SceneUpdateRequest,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
+):
+    """更新本体场景
+    
+    更新指定场景的信息，只能更新当前工作空间下的场景。
+    
+    Args:
+        scene_id: 场景ID
+        request: 场景更新请求
+        db: 数据库会话
+        current_user: 当前用户
+        
+    Returns:
+        ApiResponse: 包含更新后的场景信息
+    """
+    api_logger.info(
+        f"Scene update requested by user {current_user.id}, "
+        f"scene_id={scene_id}"
+    )
+    
+    try:
+        from uuid import UUID
+        
+        # 验证UUID格式
+        try:
+            scene_uuid = UUID(scene_id)
+        except ValueError:
+            api_logger.warning(f"Invalid scene_id format: {scene_id}")
+            return fail(BizCode.BAD_REQUEST, "请求参数无效", "无效的场景ID格式")
+        
+        # 获取当前工作空间ID
+        workspace_id = current_user.current_workspace_id
+        if not workspace_id:
+            api_logger.warning(f"User {current_user.id} has no current workspace")
+            return fail(BizCode.BAD_REQUEST, "请求参数无效", "当前用户没有工作空间")
+        
+        # 创建OntologyService实例
+        from app.core.memory.llm_tools.openai_client import OpenAIClient
+        from app.core.models.base import RedBearModelConfig
+        
+        dummy_config = RedBearModelConfig(
+            model_name="dummy",
+            provider="openai",
+            api_key="dummy",
+            base_url="https://api.openai.com/v1"
+        )
+        llm_client = OpenAIClient(model_config=dummy_config)
+        service = OntologyService(llm_client=llm_client, db=db)
+        
+        # 调用服务层更新场景
+        scene = service.update_scene(
+            scene_id=scene_uuid,
+            scene_name=request.scene_name,
+            scene_description=request.scene_description,
+            workspace_id=workspace_id
+        )
+        
+        # 构建响应
+        # 动态计算 type_num
+        type_num = len(scene.classes) if scene.classes else 0
+        
+        response = SceneResponse(
+            scene_id=scene.scene_id,
+            scene_name=scene.scene_name,
+            scene_description=scene.scene_description,
+            type_num=type_num,
+            workspace_id=scene.workspace_id,
+            created_at=scene.created_at,
+            updated_at=scene.updated_at,
+            classes_count=type_num
+        )
+        
+        api_logger.info(f"Scene updated successfully: {scene_id}")
+        
+        return success(data=response.model_dump(), msg="场景更新成功")
+        
+    except ValueError as e:
+        api_logger.warning(f"Validation error in scene update: {str(e)}")
+        return fail(BizCode.BAD_REQUEST, "请求参数无效", str(e))
+        
+    except RuntimeError as e:
+        api_logger.error(f"Runtime error in scene update: {str(e)}", exc_info=True)
+        return fail(BizCode.INTERNAL_ERROR, "场景更新失败", str(e))
+        
+    except Exception as e:
+        api_logger.error(f"Unexpected error in scene update: {str(e)}", exc_info=True)
+        return fail(BizCode.INTERNAL_ERROR, "场景更新失败", str(e))
+
+
+@router.delete("/scene/{scene_id}", response_model=ApiResponse)
+async def delete_scene(
+    scene_id: str,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
+):
+    """删除本体场景
+    
+    删除指定场景及其所有关联类型，只能删除当前工作空间下的场景。
+    
+    Args:
+        scene_id: 场景ID
+        db: 数据库会话
+        current_user: 当前用户
+        
+    Returns:
+        ApiResponse: 删除结果
+    """
+    api_logger.info(
+        f"Scene deletion requested by user {current_user.id}, "
+        f"scene_id={scene_id}"
+    )
+    
+    try:
+        from uuid import UUID
+        
+        # 验证UUID格式
+        try:
+            scene_uuid = UUID(scene_id)
+        except ValueError:
+            api_logger.warning(f"Invalid scene_id format: {scene_id}")
+            return fail(BizCode.BAD_REQUEST, "请求参数无效", "无效的场景ID格式")
+        
+        # 获取当前工作空间ID
+        workspace_id = current_user.current_workspace_id
+        if not workspace_id:
+            api_logger.warning(f"User {current_user.id} has no current workspace")
+            return fail(BizCode.BAD_REQUEST, "请求参数无效", "当前用户没有工作空间")
+        
+        # 创建OntologyService实例
+        from app.core.memory.llm_tools.openai_client import OpenAIClient
+        from app.core.models.base import RedBearModelConfig
+        
+        dummy_config = RedBearModelConfig(
+            model_name="dummy",
+            provider="openai",
+            api_key="dummy",
+            base_url="https://api.openai.com/v1"
+        )
+        llm_client = OpenAIClient(model_config=dummy_config)
+        service = OntologyService(llm_client=llm_client, db=db)
+        
+        # 调用服务层删除场景
+        success_flag = service.delete_scene(
+            scene_id=scene_uuid,
+            workspace_id=workspace_id
+        )
+        
+        api_logger.info(f"Scene deleted successfully: {scene_id}")
+        
+        return success(data={"deleted": success_flag}, msg="场景删除成功")
+        
+    except ValueError as e:
+        api_logger.warning(f"Validation error in scene deletion: {str(e)}")
+        return fail(BizCode.BAD_REQUEST, "请求参数无效", str(e))
+        
+    except RuntimeError as e:
+        api_logger.error(f"Runtime error in scene deletion: {str(e)}", exc_info=True)
+        return fail(BizCode.INTERNAL_ERROR, "场景删除失败", str(e))
+        
+    except Exception as e:
+        api_logger.error(f"Unexpected error in scene deletion: {str(e)}", exc_info=True)
+        return fail(BizCode.INTERNAL_ERROR, "场景删除失败", str(e))
+
+
+@router.get("/scenes", response_model=ApiResponse)
+async def get_scenes(
+    workspace_id: Optional[str] = None,
+    scene_name: Optional[str] = None,
+    page: Optional[int] = None,
+    pagesize: Optional[int] = None,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
+):
+    """获取场景列表（支持模糊搜索和全量查询，全量查询支持分页）
+    
+    根据是否提供 scene_name 参数，执行不同的查询：
+    - 提供 scene_name：进行模糊搜索，返回匹配的场景列表（支持分页）
+    - 不提供 scene_name：返回工作空间下的所有场景（支持分页）
+    
+    支持中文和英文的模糊匹配，不区分大小写。
+    
+    Args:
+        workspace_id: 工作空间ID（可选，默认当前用户工作空间）
+        scene_name: 场景名称关键词（可选，支持模糊匹配）
+        page: 页码（可选，从1开始）
+        pagesize: 每页数量（可选）
+        db: 数据库会话
+        current_user: 当前用户
+        
+    Returns:
+        ApiResponse: 包含场景列表和分页信息
+        
+    Examples:
+        - 模糊搜索（不分页）：GET /scenes?workspace_id=xxx&scene_name=医疗
+          输入 "医疗" 可以匹配到 "医疗场景"、"智慧医疗"、"医疗管理系统" 等
+        - 模糊搜索（分页）：GET /scenes?workspace_id=xxx&scene_name=医疗&page=1&pagesize=10
+          返回匹配 "医疗" 的第1页，每页10条数据
+        - 全量查询（不分页）：GET /scenes?workspace_id=xxx
+          返回工作空间下的所有场景
+        - 全量查询（分页）：GET /scenes?workspace_id=xxx&page=1&pagesize=10
+          返回第1页，每页10条数据
+          
+    Notes:
+        - 分页参数 page 和 pagesize 必须同时提供
+        - page 从1开始，pagesize 必须大于0
+        - 返回格式：{"items": [...], "page": {"page": 1, "pagesize": 10, "total": 100, "hasnext": true}}
+        - 不分页时，page 字段为 null
+    """
+    from app.controllers.ontology_secondary_routes import scenes_handler
+    return await scenes_handler(workspace_id, scene_name, page, pagesize, db, current_user)
+
+
+# ==================== 本体类型管理接口 ====================
+
+@router.post("/class", response_model=ApiResponse)
+async def create_class(
+    request: ClassCreateRequest,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
+):
+    """创建本体类型
+    
+    在指定场景下创建新的本体类型。
+    
+    Args:
+        request: 类型创建请求
+        db: 数据库会话
+        current_user: 当前用户
+        
+    Returns:
+        ApiResponse: 包含创建的类型信息
+    """
+    from app.controllers.ontology_secondary_routes import create_class_handler
+    return await create_class_handler(request, db, current_user)
+
+
+@router.put("/class/{class_id}", response_model=ApiResponse)
+async def update_class(
+    class_id: str,
+    request: ClassUpdateRequest,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
+):
+    """更新本体类型
+    
+    更新指定类型的信息，只能更新当前工作空间下场景的类型。
+    
+    Args:
+        class_id: 类型ID
+        request: 类型更新请求
+        db: 数据库会话
+        current_user: 当前用户
+        
+    Returns:
+        ApiResponse: 包含更新后的类型信息
+    """
+    from app.controllers.ontology_secondary_routes import update_class_handler
+    return await update_class_handler(class_id, request, db, current_user)
+
+
+@router.delete("/class/{class_id}", response_model=ApiResponse)
+async def delete_class(
+    class_id: str,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
+):
+    """删除本体类型
+    
+    删除指定类型，只能删除当前工作空间下场景的类型。
+    
+    Args:
+        class_id: 类型ID
+        db: 数据库会话
+        current_user: 当前用户
+        
+    Returns:
+        ApiResponse: 删除结果
+    """
+    from app.controllers.ontology_secondary_routes import delete_class_handler
+    return await delete_class_handler(class_id, db, current_user)
+
+
+@router.get("/classes", response_model=ApiResponse)
+async def get_classes(
+    scene_id: str,
+    class_name: Optional[str] = None,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
+):
+    """获取类型列表（支持模糊搜索和全量查询）
+    
+    根据是否提供 class_name 参数，执行不同的查询：
+    - 提供 class_name：进行模糊搜索，返回匹配的类型列表
+    - 不提供 class_name：返回场景下的所有类型
+    
+    支持中文和英文的模糊匹配，不区分大小写。
+    返回结果包含场景的基本信息（scene_name 和 scene_description）。
+    
+    Args:
+        scene_id: 场景ID（必填）
+        class_name: 类型名称关键词（可选，支持模糊匹配）
+        db: 数据库会话
+        current_user: 当前用户
+        
+    Returns:
+        ApiResponse: 包含类型列表和场景信息
+        
+    Examples:
+        - 模糊搜索：GET /classes?scene_id=xxx&class_name=患者
+          输入 "患者" 可以匹配到 "患者"、"患者信息"、"门诊患者" 等
+        - 全量查询：GET /classes?scene_id=xxx
+          返回场景下的所有类型
+          
+    Response Format:
+        {
+            "total": 3,
+            "scene_id": "xxx",
+            "scene_name": "医疗场景",
+            "scene_description": "用于医疗领域的本体建模",
+            "items": [...]
+        }
+    """
+    from app.controllers.ontology_secondary_routes import classes_handler
+    return await classes_handler(scene_id, class_name, db, current_user)
+
+
+@router.get("/class/{class_id}", response_model=ApiResponse)
+async def get_class(
+    class_id: str,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
+):
+    """获取单个本体类型
+    
+    根据类型ID获取类型的详细信息，只能查询当前工作空间下场景的类型。
+    
+    Args:
+        class_id: 类型ID
+        db: 数据库会话
+        current_user: 当前用户
+        
+    Returns:
+        ApiResponse: 包含类型详细信息
+        
+    Response Format:
+        {
+            "code": 0,
+            "msg": "查询成功",
+            "data": {
+                "class_id": "xxx",
+                "class_name": "患者",
+                "class_description": "在医疗机构中接受诊疗的个体",
+                "scene_id": "xxx",
+                "created_at": "2026-01-29T10:00:00",
+                "updated_at": "2026-01-29T10:00:00"
+            }
+        }
+    """
+    from app.controllers.ontology_secondary_routes import get_class_handler
+    return await get_class_handler(class_id, db, current_user)

api/app/controllers/ontology_secondary_routes.py

@@ -0,0 +1,611 @@
+# -*- coding: utf-8 -*-
+"""本体场景和类型路由（续）
+
+由于主Controller文件较大，将剩余路由放在此文件中。
+"""
+
+from uuid import UUID
+from typing import Optional
+
+from fastapi import Depends
+from sqlalchemy.orm import Session
+
+from app.core.error_codes import BizCode
+from app.core.logging_config import get_api_logger
+from app.core.response_utils import fail, success
+from app.db import get_db
+from app.dependencies import get_current_user
+from app.models.user_model import User
+from app.schemas.ontology_schemas import (
+    SceneResponse,
+    SceneListResponse,
+    PaginationInfo,
+    ClassCreateRequest,
+    ClassUpdateRequest,
+    ClassResponse,
+    ClassListResponse,
+    ClassBatchCreateResponse,
+)
+from app.schemas.response_schema import ApiResponse
+from app.services.ontology_service import OntologyService
+from app.core.memory.llm_tools.openai_client import OpenAIClient
+from app.core.models.base import RedBearModelConfig
+
+
+api_logger = get_api_logger()
+
+
+def _get_dummy_ontology_service(db: Session) -> OntologyService:
+    """获取OntologyService实例（不需要LLM）
+    
+    场景和类型管理不需要LLM，创建一个dummy配置。
+    """
+    dummy_config = RedBearModelConfig(
+        model_name="dummy",
+        provider="openai",
+        api_key="dummy",
+        base_url="https://api.openai.com/v1"
+    )
+    llm_client = OpenAIClient(model_config=dummy_config)
+    return OntologyService(llm_client=llm_client, db=db)
+
+
+# 这些函数将被导入到主Controller中
+
+async def scenes_handler(
+    workspace_id: Optional[str] = None,
+    scene_name: Optional[str] = None,
+    page: Optional[int] = None,
+    page_size: Optional[int] = None,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
+):
+    """获取场景列表（支持模糊搜索和全量查询，全量查询支持分页）
+    
+    当提供 scene_name 参数时，进行模糊搜索（不分页）；
+    当不提供 scene_name 参数时，返回所有场景（支持分页）。
+    
+    Args:
+        workspace_id: 工作空间ID（可选，默认当前用户工作空间）
+        scene_name: 场景名称关键词（可选，支持模糊匹配）
+        page: 页码（可选，从1开始，仅在全量查询时有效）
+        page_size: 每页数量（可选，仅在全量查询时有效）
+        db: 数据库会话
+        current_user: 当前用户
+    """
+    operation = "search" if scene_name else "list"
+    api_logger.info(
+        f"Scene {operation} requested by user {current_user.id}, "
+        f"workspace_id={workspace_id}, keyword={scene_name}, page={page}, page_size={page_size}"
+    )
+    
+    try:
+        # 确定工作空间ID
+        if workspace_id:
+            try:
+                ws_uuid = UUID(workspace_id)
+            except ValueError:
+                api_logger.warning(f"Invalid workspace_id format: {workspace_id}")
+                return fail(BizCode.BAD_REQUEST, "请求参数无效", "无效的工作空间ID格式")
+        else:
+            ws_uuid = current_user.current_workspace_id
+            if not ws_uuid:
+                api_logger.warning(f"User {current_user.id} has no current workspace")
+                return fail(BizCode.BAD_REQUEST, "请求参数无效", "当前用户没有工作空间")
+        
+        # 创建Service
+        service = _get_dummy_ontology_service(db)
+        
+        # 根据是否提供 scene_name 决定查询方式
+        if scene_name and scene_name.strip():
+            # 验证分页参数（模糊搜索也支持分页）
+            if page is not None and page < 1:
+                api_logger.warning(f"Invalid page number: {page}")
+                return fail(BizCode.BAD_REQUEST, "请求参数无效", "页码必须大于0")
+            
+            if page_size is not None and page_size < 1:
+                api_logger.warning(f"Invalid page_size: {page_size}")
+                return fail(BizCode.BAD_REQUEST, "请求参数无效", "每页数量必须大于0")
+            
+            # 如果只提供了page或page_size中的一个，返回错误
+            if (page is not None and page_size is None) or (page is None and page_size is not None):
+                api_logger.warning(f"Incomplete pagination params: page={page}, page_size={page_size}")
+                return fail(BizCode.BAD_REQUEST, "请求参数无效", "分页参数page和pagesize必须同时提供")
+            
+            # 模糊搜索场景（支持分页）
+            scenes = service.search_scenes_by_name(scene_name.strip(), ws_uuid)
+            total = len(scenes)
+            
+            # 如果提供了分页参数，进行分页处理
+            if page is not None and page_size is not None:
+                start_idx = (page - 1) * page_size
+                end_idx = start_idx + page_size
+                scenes = scenes[start_idx:end_idx]
+            
+            # 构建响应
+            items = []
+            for scene in scenes:
+                # 获取前3个class_name作为entity_type
+                entity_type = [cls.class_name for cls in scene.classes[:3]] if scene.classes else None
+                # 动态计算 type_num
+                type_num = len(scene.classes) if scene.classes else 0
+                
+                items.append(SceneResponse(
+                    scene_id=scene.scene_id,
+                    scene_name=scene.scene_name,
+                    scene_description=scene.scene_description,
+                    type_num=type_num,
+                    entity_type=entity_type,
+                    workspace_id=scene.workspace_id,
+                    created_at=scene.created_at,
+                    updated_at=scene.updated_at,
+                    classes_count=type_num
+                ))
+            
+            # 构建响应（包含分页信息）
+            if page is not None and page_size is not None:
+                # 计算是否有下一页
+                hasnext = (page * page_size) < total
+                
+                pagination_info = PaginationInfo(
+                    page=page,
+                    pagesize=page_size,
+                    total=total,
+                    hasnext=hasnext
+                )
+                response = SceneListResponse(items=items, page=pagination_info)
+            else:
+                response = SceneListResponse(items=items)
+            
+            api_logger.info(
+                f"Scene search completed: found {len(items)} scenes matching '{scene_name}' "
+                f"in workspace {ws_uuid}, total={total}"
+            )
+        else:
+            # 获取所有场景（支持分页）
+            # 验证分页参数
+            if page is not None and page < 1:
+                api_logger.warning(f"Invalid page number: {page}")
+                return fail(BizCode.BAD_REQUEST, "请求参数无效", "页码必须大于0")
+            
+            if page_size is not None and page_size < 1:
+                api_logger.warning(f"Invalid page_size: {page_size}")
+                return fail(BizCode.BAD_REQUEST, "请求参数无效", "每页数量必须大于0")
+            
+            # 如果只提供了page或page_size中的一个，返回错误
+            if (page is not None and page_size is None) or (page is None and page_size is not None):
+                api_logger.warning(f"Incomplete pagination params: page={page}, page_size={page_size}")
+                return fail(BizCode.BAD_REQUEST, "请求参数无效", "分页参数page和pagesize必须同时提供")
+            
+            scenes, total = service.list_scenes(ws_uuid, page, page_size)
+            
+            # 构建响应
+            items = []
+            for scene in scenes:
+                # 获取前3个class_name作为entity_type
+                entity_type = [cls.class_name for cls in scene.classes[:3]] if scene.classes else None
+                # 动态计算 type_num
+                type_num = len(scene.classes) if scene.classes else 0
+                
+                items.append(SceneResponse(
+                    scene_id=scene.scene_id,
+                    scene_name=scene.scene_name,
+                    scene_description=scene.scene_description,
+                    type_num=type_num,
+                    entity_type=entity_type,
+                    workspace_id=scene.workspace_id,
+                    created_at=scene.created_at,
+                    updated_at=scene.updated_at,
+                    classes_count=type_num
+                ))
+            
+            # 构建响应（包含分页信息）
+            if page is not None and page_size is not None:
+                # 计算是否有下一页
+                hasnext = (page * page_size) < total
+                
+                pagination_info = PaginationInfo(
+                    page=page,
+                    pagesize=page_size,
+                    total=total,
+                    hasnext=hasnext
+                )
+                response = SceneListResponse(items=items, page=pagination_info)
+            else:
+                response = SceneListResponse(items=items)
+            
+            api_logger.info(f"Scene list retrieved successfully, count={len(items)}, total={total}")
+        
+        return success(data=response.model_dump(mode='json'), msg="查询成功")
+        
+    except ValueError as e:
+        api_logger.warning(f"Validation error in scene {operation}: {str(e)}")
+        return fail(BizCode.BAD_REQUEST, "请求参数无效", str(e))
+        
+    except RuntimeError as e:
+        api_logger.error(f"Runtime error in scene {operation}: {str(e)}", exc_info=True)
+        return fail(BizCode.INTERNAL_ERROR, "查询失败", str(e))
+        
+    except Exception as e:
+        api_logger.error(f"Unexpected error in scene {operation}: {str(e)}", exc_info=True)
+        return fail(BizCode.INTERNAL_ERROR, "查询失败", str(e))
+
+
+# ==================== 本体类型管理接口 ====================
+
+async def create_class_handler(
+    request: ClassCreateRequest,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
+):
+    """创建本体类型（统一使用列表形式，支持单个或批量）"""
+    
+    # 根据列表长度判断是单个还是批量
+    count = len(request.classes)
+    mode = "single" if count == 1 else "batch"
+    
+    api_logger.info(
+        f"Class creation ({mode}) requested by user {current_user.id}, "
+        f"scene_id={request.scene_id}, count={count}"
+    )
+    
+    try:
+        # 获取当前工作空间ID
+        workspace_id = current_user.current_workspace_id
+        if not workspace_id:
+            api_logger.warning(f"User {current_user.id} has no current workspace")
+            return fail(BizCode.BAD_REQUEST, "请求参数无效", "当前用户没有工作空间")
+        
+        # 创建Service
+        service = _get_dummy_ontology_service(db)
+        
+        # 准备类型数据
+        classes_data = [
+            {
+                "class_name": item.class_name,
+                "class_description": item.class_description
+            }
+            for item in request.classes
+        ]
+        
+        if count == 1:
+            # 单个创建
+            class_data = classes_data[0]
+            ontology_class = service.create_class(
+                scene_id=request.scene_id,
+                class_name=class_data["class_name"],
+                class_description=class_data["class_description"],
+                workspace_id=workspace_id
+            )
+            
+            # 构建单个响应
+            response = ClassResponse(
+                class_id=ontology_class.class_id,
+                class_name=ontology_class.class_name,
+                class_description=ontology_class.class_description,
+                scene_id=ontology_class.scene_id,
+                created_at=ontology_class.created_at,
+                updated_at=ontology_class.updated_at
+            )
+            
+            api_logger.info(f"Class created successfully: {ontology_class.class_id}")
+            
+            return success(data=response.model_dump(mode='json'), msg="类型创建成功")
+            
+        else:
+            # 批量创建
+            created_classes, errors = service.create_classes_batch(
+                scene_id=request.scene_id,
+                classes=classes_data,
+                workspace_id=workspace_id
+            )
+            
+            # 构建批量响应
+            items = []
+            for ontology_class in created_classes:
+                items.append(ClassResponse(
+                    class_id=ontology_class.class_id,
+                    class_name=ontology_class.class_name,
+                    class_description=ontology_class.class_description,
+                    scene_id=ontology_class.scene_id,
+                    created_at=ontology_class.created_at,
+                    updated_at=ontology_class.updated_at
+                ))
+            
+            response = ClassBatchCreateResponse(
+                total=len(classes_data),
+                success_count=len(created_classes),
+                failed_count=len(errors),
+                items=items,
+                errors=errors if errors else None
+            )
+            
+            api_logger.info(
+                f"Batch class creation completed: "
+                f"success={len(created_classes)}, failed={len(errors)}"
+            )
+            
+            return success(data=response.model_dump(mode='json'), msg="批量创建完成")
+        
+    except ValueError as e:
+        api_logger.warning(f"Validation error in class creation: {str(e)}")
+        return fail(BizCode.BAD_REQUEST, "请求参数无效", str(e))
+        
+    except RuntimeError as e:
+        api_logger.error(f"Runtime error in class creation: {str(e)}", exc_info=True)
+        return fail(BizCode.INTERNAL_ERROR, "类型创建失败", str(e))
+        
+    except Exception as e:
+        api_logger.error(f"Unexpected error in class creation: {str(e)}", exc_info=True)
+        return fail(BizCode.INTERNAL_ERROR, "类型创建失败", str(e))
+
+
+async def update_class_handler(
+    class_id: str,
+    request: ClassUpdateRequest,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
+):
+    """更新本体类型"""
+    api_logger.info(
+        f"Class update requested by user {current_user.id}, "
+        f"class_id={class_id}"
+    )
+    
+    try:
+        # 验证UUID格式
+        try:
+            class_uuid = UUID(class_id)
+        except ValueError:
+            api_logger.warning(f"Invalid class_id format: {class_id}")
+            return fail(BizCode.BAD_REQUEST, "请求参数无效", "无效的类型ID格式")
+        
+        # 获取当前工作空间ID
+        workspace_id = current_user.current_workspace_id
+        if not workspace_id:
+            api_logger.warning(f"User {current_user.id} has no current workspace")
+            return fail(BizCode.BAD_REQUEST, "请求参数无效", "当前用户没有工作空间")
+        
+        # 创建Service
+        service = _get_dummy_ontology_service(db)
+        
+        # 更新类型
+        ontology_class = service.update_class(
+            class_id=class_uuid,
+            class_name=request.class_name,
+            class_description=request.class_description,
+            workspace_id=workspace_id
+        )
+        
+        # 构建响应
+        response = ClassResponse(
+            class_id=ontology_class.class_id,
+            class_name=ontology_class.class_name,
+            class_description=ontology_class.class_description,
+            scene_id=ontology_class.scene_id,
+            created_at=ontology_class.created_at,
+            updated_at=ontology_class.updated_at
+        )
+        
+        api_logger.info(f"Class updated successfully: {class_id}")
+        
+        return success(data=response.model_dump(mode='json'), msg="类型更新成功")
+        
+    except ValueError as e:
+        api_logger.warning(f"Validation error in class update: {str(e)}")
+        return fail(BizCode.BAD_REQUEST, "请求参数无效", str(e))
+        
+    except RuntimeError as e:
+        api_logger.error(f"Runtime error in class update: {str(e)}", exc_info=True)
+        return fail(BizCode.INTERNAL_ERROR, "类型更新失败", str(e))
+        
+    except Exception as e:
+        api_logger.error(f"Unexpected error in class update: {str(e)}", exc_info=True)
+        return fail(BizCode.INTERNAL_ERROR, "类型更新失败", str(e))
+
+
+async def delete_class_handler(
+    class_id: str,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
+):
+    """删除本体类型"""
+    api_logger.info(
+        f"Class deletion requested by user {current_user.id}, "
+        f"class_id={class_id}"
+    )
+    
+    try:
+        # 验证UUID格式
+        try:
+            class_uuid = UUID(class_id)
+        except ValueError:
+            api_logger.warning(f"Invalid class_id format: {class_id}")
+            return fail(BizCode.BAD_REQUEST, "请求参数无效", "无效的类型ID格式")
+        
+        # 获取当前工作空间ID
+        workspace_id = current_user.current_workspace_id
+        if not workspace_id:
+            api_logger.warning(f"User {current_user.id} has no current workspace")
+            return fail(BizCode.BAD_REQUEST, "请求参数无效", "当前用户没有工作空间")
+        
+        # 创建Service
+        service = _get_dummy_ontology_service(db)
+        
+        # 删除类型
+        success_flag = service.delete_class(
+            class_id=class_uuid,
+            workspace_id=workspace_id
+        )
+        
+        api_logger.info(f"Class deleted successfully: {class_id}")
+        
+        return success(data={"deleted": success_flag}, msg="类型删除成功")
+        
+    except ValueError as e:
+        api_logger.warning(f"Validation error in class deletion: {str(e)}")
+        return fail(BizCode.BAD_REQUEST, "请求参数无效", str(e))
+        
+    except RuntimeError as e:
+        api_logger.error(f"Runtime error in class deletion: {str(e)}", exc_info=True)
+        return fail(BizCode.INTERNAL_ERROR, "类型删除失败", str(e))
+        
+    except Exception as e:
+        api_logger.error(f"Unexpected error in class deletion: {str(e)}", exc_info=True)
+        return fail(BizCode.INTERNAL_ERROR, "类型删除失败", str(e))
+
+
+async def get_class_handler(
+    class_id: str,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
+):
+    """获取单个本体类型"""
+    api_logger.info(
+        f"Get class requested by user {current_user.id}, "
+        f"class_id={class_id}"
+    )
+    
+    try:
+        # 验证UUID格式
+        try:
+            class_uuid = UUID(class_id)
+        except ValueError:
+            api_logger.warning(f"Invalid class_id format: {class_id}")
+            return fail(BizCode.BAD_REQUEST, "请求参数无效", "无效的类型ID格式")
+        
+        # 获取当前工作空间ID
+        workspace_id = current_user.current_workspace_id
+        if not workspace_id:
+            api_logger.warning(f"User {current_user.id} has no current workspace")
+            return fail(BizCode.BAD_REQUEST, "请求参数无效", "当前用户没有工作空间")
+        
+        # 创建Service
+        service = _get_dummy_ontology_service(db)
+        
+        # 获取类型（会抛出ValueError如果不存在）
+        ontology_class = service.get_class_by_id(class_uuid, workspace_id)
+        
+        # 构建响应
+        response = ClassResponse(
+            class_id=ontology_class.class_id,
+            class_name=ontology_class.class_name,
+            class_description=ontology_class.class_description,
+            scene_id=ontology_class.scene_id,
+            created_at=ontology_class.created_at,
+            updated_at=ontology_class.updated_at
+        )
+        
+        api_logger.info(f"Class retrieved successfully: {class_id}")
+        
+        return success(data=response.model_dump(mode='json'), msg="查询成功")
+        
+    except ValueError as e:
+        # 类型不存在或无权限访问
+        api_logger.warning(f"Validation error in get class: {str(e)}")
+        return fail(BizCode.NOT_FOUND, "请求参数无效", str(e))
+        
+    except RuntimeError as e:
+        api_logger.error(f"Runtime error in get class: {str(e)}", exc_info=True)
+        return fail(BizCode.INTERNAL_ERROR, "查询失败", str(e))
+        
+    except Exception as e:
+        api_logger.error(f"Unexpected error in get class: {str(e)}", exc_info=True)
+        return fail(BizCode.INTERNAL_ERROR, "查询失败", str(e))
+
+
+async def classes_handler(
+    scene_id: str,
+    class_name: Optional[str] = None,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
+):
+    """获取类型列表（支持模糊搜索和全量查询）
+    
+    当提供 class_name 参数时，进行模糊搜索；
+    当不提供 class_name 参数时，返回场景下的所有类型。
+    
+    Args:
+        scene_id: 场景ID（必填）
+        class_name: 类型名称关键词（可选，支持模糊匹配）
+        db: 数据库会话
+        current_user: 当前用户
+    """
+    operation = "search" if class_name else "list"
+    api_logger.info(
+        f"Class {operation} requested by user {current_user.id}, "
+        f"keyword={class_name}, scene_id={scene_id}"
+    )
+    
+    try:
+        # 验证UUID格式
+        try:
+            scene_uuid = UUID(scene_id)
+        except ValueError:
+            api_logger.warning(f"Invalid scene_id format: {scene_id}")
+            return fail(BizCode.BAD_REQUEST, "请求参数无效", "无效的场景ID格式")
+        
+        # 获取当前工作空间ID
+        workspace_id = current_user.current_workspace_id
+        if not workspace_id:
+            api_logger.warning(f"User {current_user.id} has no current workspace")
+            return fail(BizCode.BAD_REQUEST, "请求参数无效", "当前用户没有工作空间")
+        
+        # 创建Service
+        service = _get_dummy_ontology_service(db)
+        
+        # 获取场景信息
+        scene = service.get_scene_by_id(scene_uuid, workspace_id)
+        if not scene:
+            api_logger.warning(f"Scene not found: {scene_id}")
+            return fail(BizCode.NOT_FOUND, "场景不存在", f"未找到ID为 {scene_id} 的场景")
+        
+        # 根据是否提供 class_name 决定查询方式
+        if class_name and class_name.strip():
+            # 模糊搜索类型
+            classes = service.search_classes_by_name(class_name.strip(), scene_uuid, workspace_id)
+        else:
+            # 获取所有类型
+            classes = service.list_classes_by_scene(scene_uuid, workspace_id)
+        
+        # 构建响应
+        items = []
+        for ontology_class in classes:
+            items.append(ClassResponse(
+                class_id=ontology_class.class_id,
+                class_name=ontology_class.class_name,
+                class_description=ontology_class.class_description,
+                scene_id=ontology_class.scene_id,
+                created_at=ontology_class.created_at,
+                updated_at=ontology_class.updated_at
+            ))
+        
+        response = ClassListResponse(
+            total=len(items),
+            scene_id=scene_uuid,
+            scene_name=scene.scene_name,
+            scene_description=scene.scene_description,
+            items=items
+        )
+        
+        if class_name:
+            api_logger.info(
+                f"Class search completed: found {len(items)} classes matching '{class_name}' "
+                f"in scene {scene_id}"
+            )
+        else:
+            api_logger.info(f"Class list retrieved successfully, count={len(items)}")
+        
+        return success(data=response.model_dump(mode='json'), msg="查询成功")
+        
+    except ValueError as e:
+        api_logger.warning(f"Validation error in class {operation}: {str(e)}")
+        return fail(BizCode.BAD_REQUEST, "请求参数无效", str(e))
+        
+    except RuntimeError as e:
+        api_logger.error(f"Runtime error in class {operation}: {str(e)}", exc_info=True)
+        return fail(BizCode.INTERNAL_ERROR, "查询失败", str(e))
+        
+    except Exception as e:
+        api_logger.error(f"Unexpected error in class {operation}: {str(e)}", exc_info=True)
+        return fail(BizCode.INTERNAL_ERROR, "查询失败", str(e))

api/app/core/memory/models/__init__.py

@@ -58,6 +58,12 @@ from app.core.memory.models.triplet_models import (
     TripletExtractionResponse,
 )
 
+# Ontology models
+from app.core.memory.models.ontology_models import (
+    OntologyClass,
+    OntologyExtractionResponse,
+)
+
 # Variable configuration models
 from app.core.memory.models.variate_config import (
     StatementExtractionConfig,
@@ -105,6 +111,9 @@ __all__ = [
     "Entity",
     "Triplet",
     "TripletExtractionResponse",
+    # Ontology models
+    "OntologyClass",
+    "OntologyExtractionResponse",
     # Variable configuration
     "StatementExtractionConfig",
     "ForgettingEngineConfig",

api/app/core/memory/models/ontology_models.py

@@ -0,0 +1,135 @@
+"""Models for ontology classes and extraction responses.
+
+This module contains Pydantic models for representing extracted ontology classes
+from scenario descriptions, following OWL ontology engineering standards.
+
+Classes:
+    OntologyClass: Represents an extracted ontology class
+    OntologyExtractionResponse: Response model containing extracted ontology classes
+"""
+
+from typing import List, Optional
+from uuid import uuid4
+
+from pydantic import BaseModel, ConfigDict, Field, field_validator
+
+
+class OntologyClass(BaseModel):
+    """Represents an extracted ontology class from scenario description.
+
+    An ontology class represents an abstract category or concept in a domain,
+    following OWL ontology engineering standards and naming conventions.
+
+    Attributes:
+        id: Unique string identifier for the ontology class
+        name: Name of the class in PascalCase format (e.g., 'MedicalProcedure')
+        name_chinese: Chinese translation of the class name (e.g., '医疗程序')
+        description: Textual description of the class
+        examples: List of concrete instance examples of this class
+        parent_class: Optional name of the parent class in the hierarchy
+        entity_type: Type/category of the entity (e.g., 'Person', 'Organization', 'Concept')
+        domain: Domain this class belongs to (e.g., 'Healthcare', 'Education')
+
+    Config:
+        extra: Ignore extra fields from LLM output
+    """
+    model_config = ConfigDict(extra='ignore')
+    
+    id: str = Field(
+        default_factory=lambda: uuid4().hex,
+        description="Unique identifier for the ontology class"
+    )
+    name: str = Field(
+        ...,
+        description="Name of the class in PascalCase format"
+    )
+    name_chinese: Optional[str] = Field(
+        None,
+        description="Chinese translation of the class name"
+    )
+    description: str = Field(
+        ...,
+        description="Description of the class"
+    )
+    examples: List[str] = Field(
+        default_factory=list,
+        description="List of concrete instance examples"
+    )
+    parent_class: Optional[str] = Field(
+        None,
+        description="Name of the parent class in the hierarchy"
+    )
+    entity_type: str = Field(
+        ...,
+        description="Type/category of the entity"
+    )
+    domain: str = Field(
+        ...,
+        description="Domain this class belongs to"
+    )
+
+    @field_validator('name')
+    @classmethod
+    def validate_pascal_case(cls, v: str) -> str:
+        """Validate that the class name follows PascalCase convention.
+
+        PascalCase rules:
+        - Must start with an uppercase letter
+        - Cannot contain spaces
+        - Should not contain special characters except underscores
+
+        Args:
+            v: The class name to validate
+
+        Returns:
+            The validated class name
+
+        Raises:
+            ValueError: If the name doesn't follow PascalCase convention
+        """
+        if not v:
+            raise ValueError("Class name cannot be empty")
+        
+        if not v[0].isupper():
+            raise ValueError(
+                f"Class name '{v}' must start with an uppercase letter (PascalCase)"
+            )
+        
+        if ' ' in v:
+            raise ValueError(
+                f"Class name '{v}' cannot contain spaces (PascalCase)"
+            )
+        
+        # Check for invalid characters (allow alphanumeric and underscore only)
+        if not all(c.isalnum() or c == '_' for c in v):
+            raise ValueError(
+                f"Class name '{v}' contains invalid characters. "
+                "Only alphanumeric characters and underscores are allowed"
+            )
+        
+        return v
+
+
+class OntologyExtractionResponse(BaseModel):
+    """Response model for ontology extraction from LLM.
+
+    This model represents the structured output from the LLM when
+    extracting ontology classes from scenario descriptions.
+
+    Attributes:
+        classes: List of extracted ontology classes
+        domain: Domain/field the scenario belongs to
+
+    Config:
+        extra: Ignore extra fields from LLM output
+    """
+    model_config = ConfigDict(extra='ignore')
+    
+    classes: List[OntologyClass] = Field(
+        default_factory=list,
+        description="List of extracted ontology classes"
+    )
+    domain: str = Field(
+        ...,
+        description="Domain/field the scenario belongs to"
+    )

api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/__init__.py

@@ -8,4 +8,5 @@
 - TemporalExtractor: 时间信息提取
 - EmbeddingGenerator: 嵌入向量生成
 - MemorySummaryGenerator: 记忆摘要生成
+- OntologyExtractor: 本体类提取
 """

api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/ontology_extraction.py

@@ -0,0 +1,482 @@
+"""Ontology class extraction from scenario descriptions using LLM.
+
+This module provides the OntologyExtractor class for extracting ontology classes
+from natural language scenario descriptions. It uses LLM-driven extraction combined
+with two-layer validation (string validation + OWL semantic validation).
+
+Classes:
+    OntologyExtractor: Extracts ontology classes from scenario descriptions
+"""
+
+import asyncio
+import logging
+import time
+from typing import List, Optional
+
+from app.core.memory.llm_tools.openai_client import OpenAIClient
+from app.core.memory.models.ontology_models import (
+    OntologyClass,
+    OntologyExtractionResponse,
+)
+from app.core.memory.utils.validation.ontology_validator import OntologyValidator
+from app.core.memory.utils.validation.owl_validator import OWLValidator
+from app.core.memory.utils.prompt.prompt_utils import render_ontology_extraction_prompt
+
+
+logger = logging.getLogger(__name__)
+
+
+class OntologyExtractor:
+    """Extractor for ontology classes from scenario descriptions.
+    
+    This extractor uses LLM to identify abstract classes and concepts from
+    natural language scenario descriptions, following OWL ontology engineering
+    standards. It performs two-layer validation:
+    1. String validation (naming conventions, reserved words, duplicates)
+    2. OWL semantic validation (consistency checking, circular inheritance)
+    
+    Attributes:
+        llm_client: OpenAI client for LLM calls
+        validator: String validator for class names and descriptions
+        owl_validator: OWL validator for semantic validation
+    """
+    
+    def __init__(self, llm_client: OpenAIClient):
+        """Initialize the OntologyExtractor.
+        
+        Args:
+            llm_client: OpenAIClient instance for LLM processing
+        """
+        self.llm_client = llm_client
+        self.validator = OntologyValidator()
+        self.owl_validator = OWLValidator()
+        
+        logger.info("OntologyExtractor initialized")
+    
+    async def extract_ontology_classes(
+        self,
+        scenario: str,
+        domain: Optional[str] = None,
+        max_classes: int = 15,
+        min_classes: int = 5,
+        enable_owl_validation: bool = True,
+        llm_temperature: float = 0.3,
+        llm_max_tokens: int = 2000,
+        max_description_length: int = 500,
+        timeout: Optional[float] = None,
+    ) -> OntologyExtractionResponse:
+        """Extract ontology classes from a scenario description.
+        
+        This is the main extraction method that orchestrates the entire process:
+        1. Call LLM to extract ontology classes
+        2. Perform first-layer validation (string validation and cleaning)
+        3. Perform second-layer validation (OWL semantic validation)
+        4. Filter invalid classes based on validation errors
+        5. Return validated ontology classes
+        
+        Args:
+            scenario: Natural language scenario description
+            domain: Optional domain hint (e.g., "Healthcare", "Education")
+            max_classes: Maximum number of classes to extract (default: 15)
+            min_classes: Minimum number of classes to extract (default: 5)
+            enable_owl_validation: Whether to enable OWL validation (default: True)
+            llm_temperature: LLM temperature parameter (default: 0.3)
+            llm_max_tokens: LLM max tokens parameter (default: 2000)
+            max_description_length: Maximum description length (default: 500)
+            timeout: Optional timeout in seconds for LLM call (default: None, no timeout)
+            
+        Returns:
+            OntologyExtractionResponse containing validated ontology classes
+            
+        Raises:
+            ValueError: If scenario is empty or invalid
+            asyncio.TimeoutError: If extraction times out
+            
+        Examples:
+            >>> extractor = OntologyExtractor(llm_client)
+            >>> response = await extractor.extract_ontology_classes(
+            ...     scenario="A hospital manages patient records...",
+            ...     domain="Healthcare",
+            ...     max_classes=10,
+            ...     timeout=30.0
+            ... )
+            >>> len(response.classes)
+            7
+        """
+        # Start timing
+        start_time = time.time()
+        
+        # Validate input
+        if not scenario or not scenario.strip():
+            logger.error("Scenario description is empty")
+            raise ValueError("Scenario description cannot be empty")
+        
+        scenario = scenario.strip()
+        
+        logger.info(
+            f"Starting ontology extraction - scenario_length={len(scenario)}, "
+            f"domain={domain}, max_classes={max_classes}, min_classes={min_classes}, "
+            f"timeout={timeout}"
+        )
+        
+        try:
+            # Step 1: Call LLM for extraction with timeout
+            logger.info("Step 1: Calling LLM for ontology extraction")
+            llm_start_time = time.time()
+            
+            if timeout is not None:
+                # Wrap LLM call with timeout
+                try:
+                    response = await asyncio.wait_for(
+                        self._call_llm_for_extraction(
+                            scenario=scenario,
+                            domain=domain,
+                            max_classes=max_classes,
+                            llm_temperature=llm_temperature,
+                            llm_max_tokens=llm_max_tokens,
+                        ),
+                        timeout=timeout
+                    )
+                except asyncio.TimeoutError:
+                    llm_duration = time.time() - llm_start_time
+                    logger.error(
+                        f"LLM extraction timed out after {timeout} seconds "
+                        f"(actual duration: {llm_duration:.2f}s)"
+                    )
+                    # Return empty response on timeout
+                    return OntologyExtractionResponse(
+                        classes=[],
+                        domain=domain or "Unknown",
+                    )
+            else:
+                # No timeout specified, call directly
+                response = await self._call_llm_for_extraction(
+                    scenario=scenario,
+                    domain=domain,
+                    max_classes=max_classes,
+                    llm_temperature=llm_temperature,
+                    llm_max_tokens=llm_max_tokens,
+                )
+            
+            llm_duration = time.time() - llm_start_time
+            logger.info(
+                f"LLM returned {len(response.classes)} classes in {llm_duration:.2f}s"
+            )
+            
+            # Step 2: First-layer validation (string validation and cleaning)
+            logger.info("Step 2: Performing first-layer validation (string validation)")
+            validation_start_time = time.time()
+            
+            response = self._validate_and_clean(
+                response=response,
+                max_description_length=max_description_length,
+            )
+            
+            validation_duration = time.time() - validation_start_time
+            logger.info(
+                f"After first-layer validation: {len(response.classes)} classes remain "
+                f"(validation took {validation_duration:.2f}s)"
+            )
+            
+            # Check if we have enough classes after first-layer validation
+            if len(response.classes) < min_classes:
+                logger.warning(
+                    f"Only {len(response.classes)} classes remain after validation, "
+                    f"which is below minimum of {min_classes}"
+                )
+            
+            # Step 3: Second-layer validation (OWL semantic validation)
+            if enable_owl_validation and response.classes:
+                logger.info("Step 3: Performing second-layer validation (OWL validation)")
+                owl_start_time = time.time()
+                
+                is_valid, errors, world = self.owl_validator.validate_ontology_classes(
+                    classes=response.classes,
+                )
+                
+                owl_duration = time.time() - owl_start_time
+                
+                if not is_valid:
+                    logger.warning(
+                        f"OWL validation found {len(errors)} issues in {owl_duration:.2f}s: {errors}"
+                    )
+                    
+                    # Filter invalid classes based on errors
+                    response = self._filter_invalid_classes(
+                        response=response,
+                        errors=errors,
+                    )
+                    
+                    logger.info(
+                        f"After second-layer validation: {len(response.classes)} classes remain"
+                    )
+                else:
+                    logger.info(f"OWL validation passed successfully in {owl_duration:.2f}s")
+            else:
+                if not enable_owl_validation:
+                    logger.info("Step 3: OWL validation disabled, skipping")
+                else:
+                    logger.info("Step 3: No classes to validate, skipping OWL validation")
+            
+            # Calculate total duration
+            total_duration = time.time() - start_time
+            
+            # Log extraction statistics
+            logger.info(
+                f"Ontology extraction completed - "
+                f"final_class_count={len(response.classes)}, "
+                f"domain={response.domain}, "
+                f"total_duration={total_duration:.2f}s, "
+                f"llm_duration={llm_duration:.2f}s"
+            )
+            
+            return response
+            
+        except asyncio.TimeoutError:
+            # Re-raise timeout errors
+            total_duration = time.time() - start_time
+            logger.error(
+                f"Ontology extraction timed out after {timeout} seconds "
+                f"(total duration: {total_duration:.2f}s)",
+                exc_info=True
+            )
+            raise
+        except Exception as e:
+            total_duration = time.time() - start_time
+            logger.error(
+                f"Ontology extraction failed after {total_duration:.2f}s: {str(e)}",
+                exc_info=True
+            )
+            # Return empty response on failure
+            return OntologyExtractionResponse(
+                classes=[],
+                domain=domain or "Unknown",
+            )
+    
+    async def _call_llm_for_extraction(
+        self,
+        scenario: str,
+        domain: Optional[str],
+        max_classes: int,
+        llm_temperature: float,
+        llm_max_tokens: int,
+    ) -> OntologyExtractionResponse:
+        """Call LLM to extract ontology classes from scenario.
+        
+        This method renders the extraction prompt using the Jinja2 template
+        and calls the LLM with structured output to get ontology classes.
+        
+        Args:
+            scenario: Scenario description text
+            domain: Optional domain hint
+            max_classes: Maximum number of classes to extract
+            llm_temperature: LLM temperature parameter
+            llm_max_tokens: LLM max tokens parameter
+            
+        Returns:
+            OntologyExtractionResponse from LLM
+            
+        Raises:
+            Exception: If LLM call fails
+        """
+        try:
+            # Render prompt using template
+            prompt_content = await render_ontology_extraction_prompt(
+                scenario=scenario,
+                domain=domain,
+                max_classes=max_classes,
+                json_schema=OntologyExtractionResponse.model_json_schema(),
+            )
+            
+            logger.debug(f"Rendered prompt length: {len(prompt_content)}")
+            
+            # Create messages for LLM
+            messages = [
+                {
+                    "role": "system",
+                    "content": (
+                        "You are an expert ontology engineer specializing in knowledge "
+                        "representation and OWL standards. Extract ontology classes from "
+                        "scenario descriptions following the provided instructions. "
+                        "Return valid JSON conforming to the schema."
+                    ),
+                },
+                {
+                    "role": "user",
+                    "content": prompt_content,
+                },
+            ]
+            
+            # Call LLM with structured output
+            logger.debug(
+                f"Calling LLM with temperature={llm_temperature}, "
+                f"max_tokens={llm_max_tokens}"
+            )
+            
+            response = await self.llm_client.response_structured(
+                messages=messages,
+                response_model=OntologyExtractionResponse,
+            )
+            
+            logger.info(
+                f"LLM extraction successful - extracted {len(response.classes)} classes"
+            )
+            
+            return response
+            
+        except Exception as e:
+            logger.error(
+                f"LLM extraction failed: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def _validate_and_clean(
+        self,
+        response: OntologyExtractionResponse,
+        max_description_length: int,
+    ) -> OntologyExtractionResponse:
+        """Perform first-layer validation: string validation and cleaning.
+        
+        This method validates and cleans the extracted ontology classes:
+        1. Validate class names (PascalCase, no reserved words)
+        2. Sanitize invalid class names
+        3. Truncate long descriptions
+        4. Remove duplicate classes
+        
+        Args:
+            response: OntologyExtractionResponse from LLM
+            max_description_length: Maximum description length
+            
+        Returns:
+            Cleaned OntologyExtractionResponse
+        """
+        if not response.classes:
+            logger.debug("No classes to validate")
+            return response
+        
+        logger.debug(f"Validating {len(response.classes)} classes")
+        
+        validated_classes = []
+        
+        for ontology_class in response.classes:
+            # Validate class name
+            is_valid, error_msg = self.validator.validate_class_name(
+                ontology_class.name
+            )
+            
+            if not is_valid:
+                logger.warning(
+                    f"Invalid class name '{ontology_class.name}': {error_msg}"
+                )
+                
+                # Attempt to sanitize
+                sanitized_name = self.validator.sanitize_class_name(
+                    ontology_class.name
+                )
+                
+                logger.info(
+                    f"Sanitized class name: '{ontology_class.name}' -> '{sanitized_name}'"
+                )
+                
+                # Update class name
+                ontology_class.name = sanitized_name
+                
+                # Re-validate sanitized name
+                is_valid, error_msg = self.validator.validate_class_name(
+                    sanitized_name
+                )
+                
+                if not is_valid:
+                    logger.error(
+                        f"Failed to sanitize class name '{ontology_class.name}': {error_msg}. "
+                        "Skipping this class."
+                    )
+                    continue
+            
+            # Truncate description if too long
+            if ontology_class.description:
+                original_length = len(ontology_class.description)
+                ontology_class.description = self.validator.truncate_description(
+                    ontology_class.description,
+                    max_length=max_description_length,
+                )
+                
+                if len(ontology_class.description) < original_length:
+                    logger.debug(
+                        f"Truncated description for '{ontology_class.name}': "
+                        f"{original_length} -> {len(ontology_class.description)} chars"
+                    )
+            
+            validated_classes.append(ontology_class)
+        
+        # Remove duplicates (case-insensitive)
+        original_count = len(validated_classes)
+        validated_classes = self.validator.remove_duplicates(validated_classes)
+        
+        if len(validated_classes) < original_count:
+            logger.info(
+                f"Removed {original_count - len(validated_classes)} duplicate classes"
+            )
+        
+        # Return cleaned response
+        return OntologyExtractionResponse(
+            classes=validated_classes,
+            domain=response.domain,
+        )
+    
+    def _filter_invalid_classes(
+        self,
+        response: OntologyExtractionResponse,
+        errors: List[str],
+    ) -> OntologyExtractionResponse:
+        """Filter invalid classes based on OWL validation errors.
+        
+        This method analyzes OWL validation errors and removes classes
+        that caused validation failures (e.g., circular inheritance,
+        inconsistencies).
+        
+        Args:
+            response: OntologyExtractionResponse to filter
+            errors: List of error messages from OWL validation
+            
+        Returns:
+            Filtered OntologyExtractionResponse
+        """
+        if not errors:
+            return response
+        
+        logger.debug(f"Filtering classes based on {len(errors)} OWL validation errors")
+        
+        # Extract class names mentioned in errors
+        invalid_class_names = set()
+        
+        for error in errors:
+            # Look for class names in error messages
+            for ontology_class in response.classes:
+                if ontology_class.name in error:
+                    invalid_class_names.add(ontology_class.name)
+                    logger.debug(
+                        f"Class '{ontology_class.name}' marked as invalid due to error: {error}"
+                    )
+        
+        # Filter out invalid classes
+        if invalid_class_names:
+            original_count = len(response.classes)
+            
+            filtered_classes = [
+                c for c in response.classes
+                if c.name not in invalid_class_names
+            ]
+            
+            logger.info(
+                f"Filtered out {original_count - len(filtered_classes)} invalid classes: "
+                f"{invalid_class_names}"
+            )
+            
+            return OntologyExtractionResponse(
+                classes=filtered_classes,
+                domain=response.domain,
+            )
+        
+        return response

api/app/core/memory/utils/prompt/prompt_utils.py

@@ -409,3 +409,42 @@ async def render_episodic_title_and_type_prompt(content: str) -> str:
     })
     
     return rendered_prompt
+
+
+async def render_ontology_extraction_prompt(
+    scenario: str,
+    domain: str | None = None,
+    max_classes: int = 15,
+    json_schema: dict | None = None
+) -> str:
+    """
+    Renders the ontology extraction prompt using the extract_ontology.jinja2 template.
+
+    Args:
+        scenario: The scenario description text to extract ontology classes from
+        domain: Optional domain hint for the scenario (e.g., "Healthcare", "Education")
+        max_classes: Maximum number of classes to extract (default: 15)
+        json_schema: JSON schema for the expected output format
+
+    Returns:
+        Rendered prompt content as string
+    """
+    template = prompt_env.get_template("extract_ontology.jinja2")
+    rendered_prompt = template.render(
+        scenario=scenario,
+        domain=domain,
+        max_classes=max_classes,
+        json_schema=json_schema
+    )
+    
+    # 记录渲染结果到提示日志
+    log_prompt_rendering('ontology extraction', rendered_prompt)
+    # 可选：记录模板渲染信息
+    log_template_rendering('extract_ontology.jinja2', {
+        'scenario_len': len(scenario) if scenario else 0,
+        'domain': domain,
+        'max_classes': max_classes,
+        'json_schema': 'OntologyExtractionResponse.schema'
+    })
+    
+    return rendered_prompt

api/app/core/memory/utils/prompt/prompts/extract_ontology.jinja2

@@ -0,0 +1,210 @@
+===Task===
+Extract ontology classes from the given scenario description following ontology engineering standards.
+
+===Role===
+You are a professional ontology engineer with expertise in knowledge representation and OWL (Web Ontology Language) standards. Your task is to identify abstract classes and concepts from scenario descriptions, not concrete instances.
+
+===Scenario Description===
+{{ scenario }}
+
+{% if domain -%}
+===Domain Hint===
+This scenario belongs to the **{{ domain }}** domain. Consider domain-specific concepts and terminology when extracting classes.
+{%- endif %}
+
+===Extraction Rules===
+
+**1. Abstract Classes, Not Instances:**
+- Extract abstract categories and concepts (e.g., "MedicalProcedure", "Patient", "Diagnosis")
+- Do NOT extract concrete instances (e.g., "John Smith", "Room 301", "2024-01-15")
+- Think in terms of "types of things" rather than "specific things"
+
+**2. Naming Convention (PascalCase):**
+- Use PascalCase format for the "name" field: start with uppercase letter, capitalize each word, no spaces
+- Examples: "MedicalProcedure", "HealthcareProvider", "DiagnosticTest"
+- Avoid: "medical procedure", "healthcare_provider", "diagnostic-test"
+- Use clear, descriptive names in English
+- Avoid abbreviations unless they are standard in the domain (e.g., "API", "DNA")
+- Provide Chinese translation in the "name_chinese" field (e.g., "医疗程序", "医疗服务提供者", "诊断测试")
+
+**3. Domain Relevance:**
+- Focus on classes that are central to the scenario's domain
+- Prioritize classes that represent key concepts, entities, or relationships
+- Avoid overly generic classes (e.g., "Thing", "Object") unless they have specific domain meaning
+
+**4. Class Quantity:**
+- Extract between 5 and {{ max_classes }} classes
+- Aim for a balanced set covering the main concepts in the scenario
+- Quality over quantity: prefer well-defined classes over exhaustive lists
+
+**5. Clear Descriptions:**
+- Provide concise, informative descriptions in Chinese (max 500 characters)
+- Describe what the class represents, not specific instances
+- Use clear, natural Chinese language that explains the class's role in the domain
+
+**6. Concrete Examples:**
+- Provide 2-5 concrete instance examples in Chinese for each class
+- Examples should be specific, realistic instances of the class
+- Examples help clarify the class's scope and meaning
+- Use natural Chinese language for examples
+- Example format: ["示例1", "示例2", "示例3"]
+
+**7. Class Hierarchy:**
+- Identify parent-child relationships where applicable
+- Use the parent_class field to specify inheritance
+- Parent class must be one of the extracted classes or a standard OWL class
+- Leave parent_class as null for top-level classes
+
+**8. Entity Types:**
+- Classify each class with an appropriate entity_type
+- Common types: "Person", "Organization", "Location", "Event", "Concept", "Process", "Object", "Role"
+- Choose the most specific type that applies
+
+**9. OWL Reserved Words:**
+- Do NOT use OWL reserved words as class names
+- Reserved words include: "Thing", "Nothing", "Class", "Property", "ObjectProperty", "DatatypeProperty", "AnnotationProperty", "Ontology", "Individual", "Literal"
+- If a reserved word is needed, add a domain-specific prefix (e.g., "MedicalClass" instead of "Class")
+
+**10. Language Consistency:**
+- Extract all class names in English (PascalCase format) for the "name" field
+- Provide Chinese translation for class names in the "name_chinese" field
+- Descriptions MUST be in Chinese (中文)
+- Examples MUST be in Chinese (中文)
+- Use clear, natural Chinese language for descriptions and examples
+
+===Examples===
+
+**Example 1 (Healthcare Domain):**
+Scenario: "A hospital manages patient records, schedules appointments, and coordinates medical procedures. Doctors diagnose conditions and prescribe treatments."
+
+Output:
+{
+  "classes": [
+    {
+      "name": "Patient",
+      "name_chinese": "患者",
+      "description": "在医疗机构接受医疗护理或治疗的人",
+      "examples": ["张三", "李四", "患有糖尿病的老年患者"],
+      "parent_class": null,
+      "entity_type": "Person",
+      "domain": "Healthcare"
+    },
+    {
+      "name": "MedicalProcedure",
+      "name_chinese": "医疗程序",
+      "description": "为医疗诊断或治疗而执行的系统性操作流程",
+      "examples": ["手术", "血液检查", "X光检查", "疫苗接种"],
+      "parent_class": null,
+      "entity_type": "Process",
+      "domain": "Healthcare"
+    },
+    {
+      "name": "Diagnosis",
+      "name_chinese": "诊断",
+      "description": "基于症状和检查结果对疾病或状况的识别",
+      "examples": ["糖尿病诊断", "癌症诊断", "流感诊断"],
+      "parent_class": null,
+      "entity_type": "Concept",
+      "domain": "Healthcare"
+    },
+    {
+      "name": "Doctor",
+      "name_chinese": "医生",
+      "description": "诊断和治疗患者的持证医疗专业人员",
+      "examples": ["全科医生", "外科医生", "心脏病专家"],
+      "parent_class": null,
+      "entity_type": "Role",
+      "domain": "Healthcare"
+    },
+    {
+      "name": "Treatment",
+      "name_chinese": "治疗",
+      "description": "为治愈或管理疾病状况而提供的医疗护理或疗法",
+      "examples": ["药物治疗", "物理治疗", "化疗", "手术治疗"],
+      "parent_class": null,
+      "entity_type": "Process",
+      "domain": "Healthcare"
+    }
+  ],
+  "domain": "Healthcare",
+  "namespace": "http://example.org/healthcare#"
+}
+
+**Example 2 (Education Domain):**
+Scenario: "A university offers courses taught by professors. Students enroll in programs, attend lectures, and complete assignments to earn degrees."
+
+Output:
+{
+  "classes": [
+    {
+      "name": "Student",
+      "name_chinese": "学生",
+      "description": "在教育机构注册学习的人",
+      "examples": ["本科生", "研究生", "在职学生"],
+      "parent_class": null,
+      "entity_type": "Role",
+      "domain": "Education"
+    },
+    {
+      "name": "Course",
+      "name_chinese": "课程",
+      "description": "涵盖特定学科或主题的结构化教育课程",
+      "examples": ["计算机科学导论", "微积分I", "世界历史"],
+      "parent_class": null,
+      "entity_type": "Concept",
+      "domain": "Education"
+    },
+    {
+      "name": "Professor",
+      "name_chinese": "教授",
+      "description": "教授课程并进行研究的学术教师",
+      "examples": ["助理教授", "副教授", "正教授"],
+      "parent_class": null,
+      "entity_type": "Role",
+      "domain": "Education"
+    },
+    {
+      "name": "AcademicProgram",
+      "name_chinese": "学术项目",
+      "description": "通向学位或证书的结构化课程体系",
+      "examples": ["理学学士", "文学硕士", "博士项目"],
+      "parent_class": null,
+      "entity_type": "Concept",
+      "domain": "Education"
+    },
+    {
+      "name": "Assignment",
+      "name_chinese": "作业",
+      "description": "分配给学生以评估学习成果的任务或项目",
+      "examples": ["论文", "习题集", "研究报告", "实验报告"],
+      "parent_class": null,
+      "entity_type": "Object",
+      "domain": "Education"
+    },
+    {
+      "name": "Lecture",
+      "name_chinese": "讲座",
+      "description": "由教师进行的教育性演讲或讲座",
+      "examples": ["入门讲座", "客座讲座", "在线讲座"],
+      "parent_class": null,
+      "entity_type": "Event",
+      "domain": "Education"
+    }
+  ],
+  "domain": "Education",
+  "namespace": "http://example.org/education#"
+}
+
+===Output Format===
+
+**JSON Requirements:**
+- Use only ASCII double quotes (") for JSON structure
+- Never use Chinese quotation marks ("") or Unicode quotes
+- Escape quotation marks in text with backslashes (\")
+- Ensure proper string closure and comma separation
+- No line breaks within JSON string values
+- All class names must be in PascalCase format
+- All class names must be unique (case-insensitive)
+- Extract between 5 and {{ max_classes }} classes
+
+{{ json_schema }}

api/app/core/memory/utils/validation/__init__.py

@@ -0,0 +1,10 @@
+"""Validation utilities for ontology extraction.
+
+This module provides validation classes for ontology class names,
+descriptions, and OWL compliance checking.
+"""
+
+from .ontology_validator import OntologyValidator
+from .owl_validator import OWLValidator
+
+__all__ = ['OntologyValidator', 'OWLValidator']

api/app/core/memory/utils/validation/ontology_validator.py

@@ -0,0 +1,268 @@
+"""String validation for ontology class names and descriptions.
+
+This module provides the OntologyValidator class for validating and sanitizing
+ontology class names according to OWL standards and naming conventions.
+
+Classes:
+    OntologyValidator: Validates class names, removes duplicates, and truncates descriptions
+"""
+
+import logging
+import re
+from typing import List, Tuple
+
+from app.core.memory.models.ontology_models import OntologyClass
+
+
+logger = logging.getLogger(__name__)
+
+
+class OntologyValidator:
+    """Validator for ontology class names and descriptions.
+    
+    This validator performs string-level validation including:
+    - PascalCase naming convention validation
+    - OWL reserved word checking
+    - Duplicate class name removal
+    - Description length truncation
+    
+    Attributes:
+        OWL_RESERVED_WORDS: Set of OWL reserved words that cannot be used as class names
+    """
+    
+    # OWL reserved words that cannot be used as class names
+    OWL_RESERVED_WORDS = {
+        'Thing', 'Nothing', 'Class', 'Property',
+        'ObjectProperty', 'DatatypeProperty', 'FunctionalProperty',
+        'InverseFunctionalProperty', 'TransitiveProperty', 'SymmetricProperty',
+        'AsymmetricProperty', 'ReflexiveProperty', 'IrreflexiveProperty',
+        'Restriction', 'Ontology', 'Individual', 'NamedIndividual',
+        'Annotation', 'AnnotationProperty', 'Axiom',
+        'AllDifferent', 'AllDisjointClasses', 'AllDisjointProperties',
+        'Datatype', 'DataRange', 'Literal',
+        'DeprecatedClass', 'DeprecatedProperty',
+        'Imports', 'IncompatibleWith', 'PriorVersion', 'VersionInfo',
+        'BackwardCompatibleWith', 'OntologyProperty',
+    }
+    
+    def validate_class_name(self, name: str) -> Tuple[bool, str]:
+        """Validate that a class name follows OWL naming conventions.
+        
+        Validation rules:
+        1. Must not be empty
+        2. Must start with an uppercase letter (PascalCase)
+        3. Cannot contain spaces
+        4. Can only contain alphanumeric characters and underscores
+        5. Cannot be an OWL reserved word
+        
+        Args:
+            name: The class name to validate
+            
+        Returns:
+            Tuple of (is_valid, error_message)
+            - is_valid: True if the name is valid, False otherwise
+            - error_message: Empty string if valid, error description if invalid
+            
+        Examples:
+            >>> validator = OntologyValidator()
+            >>> validator.validate_class_name("MedicalProcedure")
+            (True, "")
+            >>> validator.validate_class_name("medical procedure")
+            (False, "Class name 'medical procedure' cannot contain spaces")
+            >>> validator.validate_class_name("Thing")
+            (False, "Class name 'Thing' is an OWL reserved word")
+        """
+        logger.debug(f"Validating class name: '{name}'")
+        
+        # Check if empty
+        if not name or not name.strip():
+            error_msg = "Class name cannot be empty"
+            logger.warning(f"Validation failed: {error_msg}")
+            return False, error_msg
+        
+        name = name.strip()
+        
+        # Check if it's an OWL reserved word
+        if name in self.OWL_RESERVED_WORDS:
+            error_msg = f"Class name '{name}' is an OWL reserved word"
+            logger.warning(f"Validation failed: {error_msg}")
+            return False, error_msg
+        
+        # Check if starts with uppercase letter
+        if not name[0].isupper():
+            error_msg = f"Class name '{name}' must start with an uppercase letter (PascalCase)"
+            logger.warning(f"Validation failed: {error_msg}")
+            return False, error_msg
+        
+        # Check for spaces
+        if ' ' in name:
+            error_msg = f"Class name '{name}' cannot contain spaces"
+            logger.warning(f"Validation failed: {error_msg}")
+            return False, error_msg
+        
+        # Check for invalid characters (only alphanumeric and underscore allowed)
+        if not re.match(r'^[A-Za-z0-9_]+$', name):
+            error_msg = f"Class name '{name}' contains invalid characters. Only alphanumeric characters and underscores are allowed"
+            logger.warning(f"Validation failed: {error_msg}")
+            return False, error_msg
+        
+        logger.debug(f"Class name '{name}' is valid")
+        return True, ""
+    
+    def sanitize_class_name(self, name: str) -> str:
+        """Attempt to sanitize an invalid class name into a valid format.
+        
+        Sanitization steps:
+        1. Strip whitespace
+        2. Remove invalid characters
+        3. Replace spaces with empty string (PascalCase)
+        4. Capitalize first letter of each word
+        5. If result is empty or starts with number, prefix with 'Class'
+        
+        Args:
+            name: The class name to sanitize
+            
+        Returns:
+            Sanitized class name that should pass validation
+            
+        Examples:
+            >>> validator = OntologyValidator()
+            >>> validator.sanitize_class_name("medical procedure")
+            'MedicalProcedure'
+            >>> validator.sanitize_class_name("patient-record")
+            'PatientRecord'
+            >>> validator.sanitize_class_name("123invalid")
+            'Class123Invalid'
+        """
+        logger.debug(f"Sanitizing class name: '{name}'")
+        
+        if not name or not name.strip():
+            logger.warning("Empty class name provided for sanitization, returning 'UnnamedClass'")
+            return "UnnamedClass"
+        
+        # Strip whitespace
+        name = name.strip()
+        original_name = name
+        
+        # Split on spaces, hyphens, and underscores, then capitalize each word
+        words = re.split(r'[\s\-_]+', name)
+        
+        # Capitalize first letter of each word and keep rest as is
+        sanitized_words = []
+        for word in words:
+            if word:
+                # Remove non-alphanumeric characters except underscore
+                clean_word = re.sub(r'[^A-Za-z0-9_]', '', word)
+                if clean_word:
+                    # Capitalize first letter
+                    sanitized_words.append(clean_word[0].upper() + clean_word[1:])
+        
+        # Join words
+        sanitized = ''.join(sanitized_words)
+        
+        # If empty or starts with number, prefix with 'Class'
+        if not sanitized or sanitized[0].isdigit():
+            sanitized = 'Class' + sanitized
+            logger.info(f"Prefixed class name with 'Class': '{original_name}' -> '{sanitized}'")
+        
+        # If it's a reserved word, append 'Class' suffix
+        if sanitized in self.OWL_RESERVED_WORDS:
+            sanitized = sanitized + 'Class'
+            logger.info(f"Appended 'Class' suffix to reserved word: '{original_name}' -> '{sanitized}'")
+        
+        logger.info(f"Sanitized class name: '{original_name}' -> '{sanitized}'")
+        return sanitized
+    
+    def remove_duplicates(self, classes: List[OntologyClass]) -> List[OntologyClass]:
+        """Remove duplicate ontology classes based on case-insensitive name comparison.
+        
+        When duplicates are found, keeps the first occurrence and discards subsequent ones.
+        Comparison is case-insensitive to catch variations like 'Patient' and 'patient'.
+        
+        Args:
+            classes: List of OntologyClass objects
+            
+        Returns:
+            List of OntologyClass objects with duplicates removed
+            
+        Examples:
+            >>> validator = OntologyValidator()
+            >>> classes = [
+            ...     OntologyClass(name="Patient", description="A patient", entity_type="Person", domain="Healthcare"),
+            ...     OntologyClass(name="patient", description="Another patient", entity_type="Person", domain="Healthcare"),
+            ...     OntologyClass(name="Doctor", description="A doctor", entity_type="Person", domain="Healthcare"),
+            ... ]
+            >>> unique = validator.remove_duplicates(classes)
+            >>> len(unique)
+            2
+            >>> [c.name for c in unique]
+            ['Patient', 'Doctor']
+        """
+        if not classes:
+            logger.debug("No classes to check for duplicates")
+            return classes
+        
+        logger.debug(f"Checking {len(classes)} classes for duplicates")
+        
+        seen_names = set()
+        unique_classes = []
+        duplicates_found = []
+        
+        for ontology_class in classes:
+            # Use lowercase for comparison
+            name_lower = ontology_class.name.lower()
+            
+            if name_lower not in seen_names:
+                seen_names.add(name_lower)
+                unique_classes.append(ontology_class)
+            else:
+                duplicates_found.append(ontology_class.name)
+                logger.debug(f"Duplicate class found and removed: '{ontology_class.name}'")
+        
+        if duplicates_found:
+            logger.info(
+                f"Removed {len(duplicates_found)} duplicate classes: {duplicates_found}"
+            )
+        else:
+            logger.debug("No duplicate classes found")
+        
+        return unique_classes
+    
+    def truncate_description(self, description: str, max_length: int = 500) -> str:
+        """Truncate a description to a maximum length.
+        
+        If the description exceeds max_length, it will be truncated and
+        an ellipsis (...) will be appended to indicate truncation.
+        
+        Args:
+            description: The description text to truncate
+            max_length: Maximum allowed length (default: 500)
+            
+        Returns:
+            Truncated description string
+            
+        Examples:
+            >>> validator = OntologyValidator()
+            >>> long_desc = "A" * 600
+            >>> truncated = validator.truncate_description(long_desc, max_length=500)
+            >>> len(truncated)
+            500
+            >>> truncated.endswith("...")
+            True
+        """
+        if not description:
+            return ""
+        
+        if len(description) <= max_length:
+            return description
+        
+        # Truncate and add ellipsis
+        # Reserve 3 characters for "..."
+        truncate_at = max_length - 3
+        truncated = description[:truncate_at] + "..."
+        
+        logger.debug(
+            f"Truncated description from {len(description)} to {len(truncated)} characters"
+        )
+        
+        return truncated

api/app/core/memory/utils/validation/owl_validator.py

@@ -0,0 +1,585 @@
+"""OWL semantic validation for ontology classes using Owlready2.
+
+This module provides the OWLValidator class for validating ontology classes
+against OWL standards using the Owlready2 library. It performs semantic
+validation including consistency checking, circular inheritance detection,
+and OWL file export.
+
+Classes:
+    OWLValidator: Validates ontology classes using OWL reasoning and exports to OWL formats
+"""
+
+import logging
+from typing import List, Optional, Tuple
+
+from owlready2 import (
+    World,
+    Thing,
+    get_ontology,
+    sync_reasoner_pellet,
+    OwlReadyInconsistentOntologyError,
+)
+
+from app.core.memory.models.ontology_models import OntologyClass
+logger = logging.getLogger(__name__)
+
+
+class OWLValidator:
+    """Validator for OWL semantic validation of ontology classes.
+    
+    This validator performs semantic-level validation using Owlready2 including:
+    - Creating OWL classes from ontology class definitions
+    - Running consistency checking with Pellet reasoner
+    - Detecting circular inheritance
+    - Validating Protégé compatibility
+    - Exporting ontologies to various OWL formats (RDF/XML, Turtle, N-Triples)
+    
+    Attributes:
+        base_namespace: Base URI for the ontology namespace
+    """
+    
+    def __init__(self, base_namespace: str = "http://example.org/ontology#"):
+        """Initialize the OWL validator.
+        
+        Args:
+            base_namespace: Base URI for the ontology namespace (default: http://example.org/ontology#)
+        """
+        self.base_namespace = base_namespace
+    
+    def validate_ontology_classes(
+        self,
+        classes: List[OntologyClass],
+    ) -> Tuple[bool, List[str], Optional[World]]:
+        """Validate extracted ontology classes against OWL standards.
+        
+        This method creates an OWL ontology from the provided classes using Owlready2,
+        runs consistency checking with the Pellet reasoner, and detects common issues
+        like circular inheritance.
+        
+        Args:
+            classes: List of OntologyClass objects to validate
+            
+        Returns:
+            Tuple of (is_valid, error_messages, world):
+            - is_valid: True if ontology is valid and consistent, False otherwise
+            - error_messages: List of error/warning messages
+            - world: Owlready2 World object containing the ontology (None if validation failed)
+            
+        Examples:
+            >>> validator = OWLValidator()
+            >>> classes = [
+            ...     OntologyClass(name="Patient", description="A patient", entity_type="Person", domain="Healthcare"),
+            ...     OntologyClass(name="Doctor", description="A doctor", entity_type="Person", domain="Healthcare"),
+            ... ]
+            >>> is_valid, errors, world = validator.validate_ontology_classes(classes)
+            >>> is_valid
+            True
+            >>> len(errors)
+            0
+        """
+        if not classes:
+            return False, ["No classes provided for validation"], None
+        
+        errors = []
+        
+        try:
+            # Create a new world (isolated ontology environment)
+            world = World()
+            
+            # Use a proper ontology IRI
+            # Owlready2 expects the IRI to end with .owl or similar
+            onto_iri = self.base_namespace.rstrip('#/')
+            if not onto_iri.endswith('.owl'):
+                onto_iri = onto_iri + '.owl'
+            
+            # Create ontology
+            onto = world.get_ontology(onto_iri)
+            
+            with onto:
+                # Dictionary to store created OWL classes for parent reference
+                owl_classes = {}
+                
+                # First pass: Create all classes without parent relationships
+                for ontology_class in classes:
+                    try:
+                        # Create OWL class dynamically using type() with Thing as base
+                        # The key is to NOT set namespace in the dict, let Owlready2 handle it
+                        owl_class = type(
+                            ontology_class.name,  # Class name
+                            (Thing,),              # Base classes
+                            {}                     # Class dict (empty, let Owlready2 manage)
+                        )
+                        
+                        # Add label (rdfs:label) - include both English and Chinese names
+                        labels = [ontology_class.name]
+                        if ontology_class.name_chinese:
+                            labels.append(ontology_class.name_chinese)
+                        owl_class.label = labels
+                        
+                        # Add comment (rdfs:comment) with description
+                        if ontology_class.description:
+                            owl_class.comment = [ontology_class.description]
+                        
+                        # Store for parent relationship setup
+                        owl_classes[ontology_class.name] = owl_class
+                        
+                        logger.debug(
+                            f"Created OWL class: {ontology_class.name} "
+                            f"(Chinese: {ontology_class.name_chinese}) "
+                            f"IRI: {owl_class.iri if hasattr(owl_class, 'iri') else 'N/A'}"
+                        )
+                        
+                    except Exception as e:
+                        error_msg = f"Failed to create OWL class '{ontology_class.name}': {str(e)}"
+                        errors.append(error_msg)
+                        logger.error(error_msg, exc_info=True)
+                
+                # Second pass: Set up parent relationships
+                for ontology_class in classes:
+                    if ontology_class.parent_class and ontology_class.name in owl_classes:
+                        parent_name = ontology_class.parent_class
+                        
+                        # Check if parent exists
+                        if parent_name in owl_classes:
+                            try:
+                                child_class = owl_classes[ontology_class.name]
+                                parent_class = owl_classes[parent_name]
+                                
+                                # Set parent by modifying is_a
+                                child_class.is_a = [parent_class]
+                                
+                                logger.debug(
+                                    f"Set parent relationship: {ontology_class.name} -> {parent_name}"
+                                )
+                                
+                            except Exception as e:
+                                error_msg = (
+                                    f"Failed to set parent relationship "
+                                    f"'{ontology_class.name}' -> '{parent_name}': {str(e)}"
+                                )
+                                errors.append(error_msg)
+                                logger.warning(error_msg)
+                        else:
+                            warning_msg = (
+                                f"Parent class '{parent_name}' not found for '{ontology_class.name}'"
+                            )
+                            errors.append(warning_msg)
+                            logger.warning(warning_msg)
+                
+                # Check for circular inheritance
+                for class_name, owl_class in owl_classes.items():
+                    if self._has_circular_inheritance(owl_class):
+                        error_msg = f"Circular inheritance detected for class '{class_name}'"
+                        errors.append(error_msg)
+                        logger.error(error_msg)
+            
+            # Run consistency checking with Pellet reasoner
+            try:
+                logger.info("Running Pellet reasoner for consistency checking...")
+                sync_reasoner_pellet(world, infer_property_values=True, infer_data_property_values=True)
+                logger.info("Consistency check passed")
+                
+            except OwlReadyInconsistentOntologyError as e:
+                error_msg = f"Ontology is inconsistent: {str(e)}"
+                errors.append(error_msg)
+                logger.error(error_msg)
+                return False, errors, world
+                
+            except Exception as e:
+                # Reasoner errors are often due to Java not being installed or configured
+                # Log as warning but don't fail validation - ontology structure is still valid
+                warning_msg = f"Reasoner check skipped: {str(e)}"
+                if str(e).strip():  # Only log if there's an actual error message
+                    logger.warning(warning_msg)
+                else:
+                    logger.warning("Reasoner check skipped: Java may not be installed or configured")
+                # Continue - ontology structure is valid even without reasoner check
+            
+            # If we have errors (excluding warnings), validation failed
+            is_valid = len(errors) == 0
+            
+            return is_valid, errors, world
+            
+        except Exception as e:
+            error_msg = f"OWL validation failed: {str(e)}"
+            errors.append(error_msg)
+            logger.error(error_msg, exc_info=True)
+            return False, errors, None
+    
+    def _has_circular_inheritance(self, owl_class) -> bool:
+        """Check if an OWL class has circular inheritance.
+        
+        Circular inheritance occurs when a class inherits from itself through
+        a chain of parent relationships (e.g., A -> B -> C -> A).
+        
+        Args:
+            owl_class: Owlready2 class object to check
+            
+        Returns:
+            True if circular inheritance is detected, False otherwise
+        """
+        visited = set()
+        current = owl_class
+        
+        while current:
+            # Get class IRI or name as identifier
+            class_id = str(current.iri) if hasattr(current, 'iri') else str(current)
+            
+            if class_id in visited:
+                # Found a cycle
+                return True
+            
+            visited.add(class_id)
+            
+            # Get parent classes (is_a relationship)
+            parents = getattr(current, 'is_a', [])
+            
+            # Filter out Thing and other base classes
+            parent_classes = [p for p in parents if p != Thing and hasattr(p, 'is_a')]
+            
+            if not parent_classes:
+                # No more parents, no cycle
+                break
+            
+            # Check first parent (in single inheritance)
+            current = parent_classes[0] if parent_classes else None
+        
+        return False
+    
+    def export_to_owl(
+        self,
+        world: World,
+        output_path: Optional[str] = None,
+        format: str = "rdfxml",
+        classes: Optional[List] = None
+    ) -> str:
+        """Export ontology to OWL file in specified format.
+        
+        Supported formats:
+        - rdfxml: RDF/XML format (default, most compatible)
+        - turtle: Turtle format (more readable)
+        - ntriples: N-Triples format (simplest)
+        - json: JSON format (simplified, human-readable)
+        
+        Args:
+            world: Owlready2 World object containing the ontology
+            output_path: Optional file path to save the ontology (if None, returns string)
+            format: Export format - "rdfxml", "turtle", "ntriples", or "json" (default: "rdfxml")
+            classes: Optional list of OntologyClass objects (required for json format)
+            
+        Returns:
+            String representation of the exported ontology
+            
+        Raises:
+            ValueError: If format is not supported
+            RuntimeError: If export fails
+            
+        Examples:
+            >>> validator = OWLValidator()
+            >>> is_valid, errors, world = validator.validate_ontology_classes(classes)
+            >>> owl_content = validator.export_to_owl(world, "ontology.owl", format="rdfxml")
+        """
+        # Validate format
+        valid_formats = ["rdfxml", "turtle", "ntriples", "json"]
+        if format not in valid_formats:
+            raise ValueError(
+                f"Unsupported format '{format}'. Must be one of: {', '.join(valid_formats)}"
+            )
+        
+        # JSON format doesn't need OWL processing
+        if format == "json":
+            if not classes:
+                raise ValueError("Classes list is required for JSON format export")
+            return self._export_to_json(classes)
+        
+        # For OWL formats, world is required
+        if not world:
+            raise ValueError("World object is None. Cannot export ontology.")
+        
+        # Note: Owlready2 has issues with turtle format export
+        # We'll handle it specially by converting from rdfxml
+        use_conversion = (format == "turtle")
+        
+        try:
+            # Get all ontologies in the world
+            ontologies = list(world.ontologies.values())
+            
+            if not ontologies:
+                raise RuntimeError("No ontologies found in world")
+            
+            # Find the ontology with classes (skip anonymous/empty ontologies)
+            onto = None
+            for ont in ontologies:
+                classes_count = len(list(ont.classes()))
+                logger.debug(f"Checking ontology {ont.base_iri}: {classes_count} classes")
+                if classes_count > 0:
+                    onto = ont
+                    break
+            
+            # If no ontology with classes found, use the last non-anonymous one
+            if onto is None:
+                for ont in reversed(ontologies):
+                    if ont.base_iri != "http://anonymous/":
+                        onto = ont
+                        break
+            
+            # If still no ontology, use the first one
+            if onto is None:
+                onto = ontologies[0]
+            
+            # Log ontology contents for debugging
+            logger.info(f"Ontology IRI: {onto.base_iri}")
+            logger.info(f"Ontology contains {len(list(onto.classes()))} classes")
+            
+            # List all classes in the ontology
+            all_classes = list(onto.classes())
+            for cls in all_classes:
+                logger.info(f"Class in ontology: {cls.name} (IRI: {cls.iri})")
+                if hasattr(cls, 'label'):
+                    logger.debug(f"  Labels: {cls.label}")
+                if hasattr(cls, 'comment'):
+                    logger.debug(f"  Comments: {cls.comment}")
+            
+            if len(all_classes) == 0:
+                logger.warning("No classes found in ontology! This may indicate a problem with class creation.")
+            
+            if output_path:
+                # Save to file
+                export_format = "rdfxml" if use_conversion else format
+                logger.info(f"Exporting ontology to {output_path} in {export_format} format")
+                onto.save(file=output_path, format=export_format)
+                
+                # Read back the file content to return
+                with open(output_path, 'r', encoding='utf-8') as f:
+                    content = f.read()
+                
+                # Convert to turtle if needed
+                if use_conversion:
+                    content = self._convert_to_turtle(content)
+                
+                logger.info(f"Successfully exported ontology to {output_path}")
+                
+                # Format the content for better readability
+                content = self._format_owl_content(content, format)
+                
+                return content
+            else:
+                # Export to string (save to temporary location and read)
+                import tempfile
+                import os
+                
+                with tempfile.NamedTemporaryFile(mode='w', suffix='.owl', delete=False) as tmp:
+                    tmp_path = tmp.name
+                
+                try:
+                    export_format = "rdfxml" if use_conversion else format
+                    onto.save(file=tmp_path, format=export_format)
+                    
+                    with open(tmp_path, 'r', encoding='utf-8') as f:
+                        content = f.read()
+                    
+                    # Convert to turtle if needed
+                    if use_conversion:
+                        content = self._convert_to_turtle(content)
+                    
+                    # Format the content for better readability
+                    content = self._format_owl_content(content, format)
+                    
+                    return content
+                    
+                finally:
+                    # Clean up temporary file
+                    if os.path.exists(tmp_path):
+                        os.remove(tmp_path)
+                        
+        except Exception as e:
+            error_msg = f"Failed to export ontology: {str(e)}"
+            logger.error(error_msg, exc_info=True)
+            raise RuntimeError(error_msg) from e
+    
+    def _export_to_json(self, classes: List) -> str:
+        """Export ontology classes to simplified JSON format.
+        
+        This format is more compact and easier to parse than OWL XML.
+        
+        Args:
+            classes: List of OntologyClass objects
+            
+        Returns:
+            JSON string representation (compact format)
+        """
+        import json
+        
+        result = {
+            "ontology": {
+                "namespace": self.base_namespace,
+                "classes": []
+            }
+        }
+        
+        for cls in classes:
+            class_data = {
+                "name": cls.name,
+                "name_chinese": cls.name_chinese,
+                "description": cls.description,
+                "entity_type": cls.entity_type,
+                "domain": cls.domain,
+                "parent_class": cls.parent_class,
+                "examples": cls.examples if hasattr(cls, 'examples') else []
+            }
+            result["ontology"]["classes"].append(class_data)
+        
+        # 使用紧凑格式：无缩进，使用分隔符减少空格
+        return json.dumps(result, ensure_ascii=False, separators=(',', ':'))
+    
+    def _convert_to_turtle(self, rdfxml_content: str) -> str:
+        """Convert RDF/XML content to Turtle format using rdflib.
+        
+        Args:
+            rdfxml_content: RDF/XML format content
+            
+        Returns:
+            Turtle format content
+        """
+        try:
+            from rdflib import Graph
+            
+            # Parse RDF/XML
+            g = Graph()
+            g.parse(data=rdfxml_content, format="xml")
+            
+            # Serialize to Turtle
+            turtle_content = g.serialize(format="turtle")
+            
+            # Handle bytes vs string
+            if isinstance(turtle_content, bytes):
+                turtle_content = turtle_content.decode('utf-8')
+            
+            return turtle_content
+            
+        except ImportError:
+            logger.warning(
+                "rdflib is not installed. Cannot convert to Turtle format. "
+                "Install with: pip install rdflib"
+            )
+            return rdfxml_content
+        except Exception as e:
+            logger.error(f"Failed to convert to Turtle format: {e}")
+            return rdfxml_content
+    
+    def _format_owl_content(self, content: str, format: str) -> str:
+        """Format OWL content for better readability.
+        
+        Args:
+            content: Raw OWL content string
+            format: Format type (rdfxml, turtle, ntriples)
+            
+        Returns:
+            Formatted OWL content string
+        """
+        if format == "rdfxml":
+            # Format XML with proper indentation
+            try:
+                import xml.dom.minidom as minidom
+                dom = minidom.parseString(content)
+                # Pretty print with 2-space indentation
+                formatted = dom.toprettyxml(indent="  ", encoding="utf-8").decode("utf-8")
+                
+                # Remove extra blank lines
+                lines = []
+                prev_blank = False
+                for line in formatted.split('\n'):
+                    is_blank = not line.strip()
+                    if not (is_blank and prev_blank):  # Skip consecutive blank lines
+                        lines.append(line)
+                    prev_blank = is_blank
+                
+                formatted = '\n'.join(lines)
+                
+                return formatted
+            except Exception as e:
+                logger.warning(f"Failed to format XML content: {e}")
+                return content
+        
+        elif format == "turtle":
+            # Turtle format is already relatively readable
+            # Just ensure consistent line endings and not empty
+            if not content or content.strip() == "":
+                logger.warning("Turtle content is empty, this may indicate an export issue")
+            return content.strip() + '\n' if content.strip() else content
+        
+        elif format == "ntriples":
+            # N-Triples format is line-based, ensure proper line endings
+            return content.strip() + '\n' if content.strip() else content
+        
+        return content
+    
+    def validate_with_protege_compatibility(
+        self,
+        classes: List[OntologyClass]
+    ) -> Tuple[bool, List[str]]:
+        """Validate that ontology classes are compatible with Protégé editor.
+        
+        Protégé compatibility checks:
+        - Class names are valid OWL identifiers
+        - No special characters that Protégé cannot handle
+        - Namespace is properly formatted
+        - Labels and comments are properly encoded
+        
+        Args:
+            classes: List of OntologyClass objects to validate
+            
+        Returns:
+            Tuple of (is_compatible, warnings):
+            - is_compatible: True if compatible with Protégé, False otherwise
+            - warnings: List of compatibility warning messages
+            
+        Examples:
+            >>> validator = OWLValidator()
+            >>> classes = [OntologyClass(name="Patient", description="A patient", entity_type="Person", domain="Healthcare")]
+            >>> is_compatible, warnings = validator.validate_with_protege_compatibility(classes)
+            >>> is_compatible
+            True
+        """
+        warnings = []
+        
+        # Check namespace format
+        if not self.base_namespace.startswith(('http://', 'https://')):
+            warnings.append(
+                f"Namespace '{self.base_namespace}' should start with http:// or https:// "
+                "for Protégé compatibility"
+            )
+        
+        if not self.base_namespace.endswith(('#', '/')):
+            warnings.append(
+                f"Namespace '{self.base_namespace}' should end with # or / "
+                "for Protégé compatibility"
+            )
+        
+        # Check each class
+        for ontology_class in classes:
+            # Check for special characters that might cause issues
+            if any(char in ontology_class.name for char in ['<', '>', '"', '{', '}', '|', '^', '`']):
+                warnings.append(
+                    f"Class name '{ontology_class.name}' contains special characters "
+                    "that may cause issues in Protégé"
+                )
+            
+            # Check description length (Protégé can handle long descriptions but may display poorly)
+            if ontology_class.description and len(ontology_class.description) > 1000:
+                warnings.append(
+                    f"Class '{ontology_class.name}' has a very long description ({len(ontology_class.description)} chars) "
+                    "which may display poorly in Protégé"
+                )
+            
+            # Check for non-ASCII characters (Protégé supports them but encoding issues may occur)
+            if not ontology_class.name.isascii():
+                warnings.append(
+                    f"Class name '{ontology_class.name}' contains non-ASCII characters "
+                    "which may cause encoding issues in some Protégé versions"
+                )
+        
+        # If no warnings, it's compatible
+        is_compatible = len(warnings) == 0
+        
+        return is_compatible, warnings

api/app/models/__init__.py

@@ -28,6 +28,10 @@ from .tool_model import (
     ToolExecution, ToolType, ToolStatus, AuthType, ExecutionStatus
 )
 from .memory_perceptual_model import MemoryPerceptualModel
+from .ontology_scene import OntologyScene
+from .ontology_class import OntologyClass
+from .ontology_scene import OntologyScene
+from .ontology_class import OntologyClass
 
 __all__ = [
     "Tenants",

api/app/models/ontology_class.py

@@ -0,0 +1,40 @@
+# -*- coding: utf-8 -*-
+"""本体类型模型
+
+本模块定义本体类型的数据模型。
+
+Classes:
+    OntologyClass: 本体类型表模型
+"""
+
+import datetime
+import uuid
+from sqlalchemy import Column, String, DateTime, Text, ForeignKey
+from sqlalchemy.dialects.postgresql import UUID
+from sqlalchemy.orm import relationship
+from app.db import Base
+
+
+class OntologyClass(Base):
+    """本体类型表 - 用于存储某个场景提取出来的本体类型信息"""
+    __tablename__ = "ontology_class"
+
+    # 主键
+    class_id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4, index=True, comment="类型ID")
+
+    # 类型信息
+    class_name = Column(String(200), nullable=False, comment="类型名称")
+    class_description = Column(Text, nullable=True, comment="类型描述")
+
+    # 外键：关联到本体场景
+    scene_id = Column(UUID(as_uuid=True), ForeignKey("ontology_scene.scene_id", ondelete="CASCADE"), nullable=False, index=True, comment="所属场景ID")
+
+    # 时间戳
+    created_at = Column(DateTime, default=datetime.datetime.now, nullable=False, comment="创建时间")
+    updated_at = Column(DateTime, default=datetime.datetime.now, onupdate=datetime.datetime.now, nullable=False, comment="更新时间")
+
+    # 关系：类型属于某个场景
+    scene = relationship("OntologyScene", back_populates="classes")
+
+    def __repr__(self):
+        return f"<OntologyClass(id={self.class_id}, name={self.class_name}, scene_id={self.scene_id})>"

api/app/models/ontology_scene.py

@@ -0,0 +1,43 @@
+# -*- coding: utf-8 -*-
+"""本体场景模型
+
+本模块定义本体场景的数据模型。
+
+Classes:
+    OntologyScene: 本体场景表模型
+"""
+
+import datetime
+import uuid
+from sqlalchemy import Column, String, DateTime, Integer, Text, ForeignKey, UniqueConstraint
+from sqlalchemy.dialects.postgresql import UUID
+from sqlalchemy.orm import relationship
+from app.db import Base
+
+
+class OntologyScene(Base):
+    """本体场景表 - 用于存储本体场景下不同的类型信息"""
+    __tablename__ = "ontology_scene"
+    __table_args__ = (
+        UniqueConstraint('workspace_id', 'scene_name', name='uq_workspace_scene_name'),
+    )
+
+    # 主键
+    scene_id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4, index=True, comment="场景ID")
+
+    # 场景信息
+    scene_name = Column(String(200), nullable=False, comment="场景名称")
+    scene_description = Column(Text, nullable=True, comment="场景描述")
+
+    # 外键：关联到工作空间
+    workspace_id = Column(UUID(as_uuid=True), ForeignKey("workspaces.id", ondelete="CASCADE"), nullable=False, index=True, comment="所属工作空间ID")
+
+    # 时间戳
+    created_at = Column(DateTime, default=datetime.datetime.now, nullable=False, comment="创建时间")
+    updated_at = Column(DateTime, default=datetime.datetime.now, onupdate=datetime.datetime.now, nullable=False, comment="更新时间")
+
+    # 关系：一个场景可以有多个类型
+    classes = relationship("OntologyClass", back_populates="scene", cascade="all, delete-orphan")
+
+    def __repr__(self):
+        return f"<OntologyScene(id={self.scene_id}, name={self.scene_name})>"

api/app/repositories/ontology_class_repository.py

@@ -0,0 +1,404 @@
+# -*- coding: utf-8 -*-
+"""本体类型Repository层
+
+本模块提供本体类型的数据访问层实现。
+
+Classes:
+    OntologyClassRepository: 本体类型数据访问类
+"""
+
+import logging
+from typing import List, Optional
+from uuid import UUID
+
+from sqlalchemy.orm import Session, joinedload
+
+from app.core.logging_config import get_db_logger
+from app.models.ontology_class import OntologyClass
+from app.models.ontology_scene import OntologyScene
+
+
+logger = get_db_logger()
+
+
+class OntologyClassRepository:
+    """本体类型Repository
+    
+    提供本体类型的CRUD操作和权限检查。
+    
+    Attributes:
+        db: SQLAlchemy数据库会话
+    """
+    
+    def __init__(self, db: Session):
+        """初始化Repository
+        
+        Args:
+            db: SQLAlchemy数据库会话
+        """
+        self.db = db
+    
+    def create(self, class_data: dict, scene_id: UUID) -> OntologyClass:
+        """创建本体类型
+        
+        Args:
+            class_data: 类型数据字典，包含class_name和class_description
+            scene_id: 所属场景ID
+            
+        Returns:
+            OntologyClass: 创建的类型对象
+            
+        Raises:
+            Exception: 数据库操作失败
+            
+        Examples:
+            >>> repo = OntologyClassRepository(db)
+            >>> ontology_class = repo.create(
+            ...     {"class_name": "患者", "class_description": "描述"},
+            ...     scene_id
+            ... )
+        """
+        try:
+            logger.info(
+                f"Creating ontology class - "
+                f"name={class_data.get('class_name')}, "
+                f"scene_id={scene_id}"
+            )
+            
+            ontology_class = OntologyClass(
+                class_name=class_data.get("class_name"),
+                class_description=class_data.get("class_description"),
+                scene_id=scene_id
+            )
+            
+            self.db.add(ontology_class)
+            self.db.flush()  # 获取ID但不提交
+            
+            logger.info(
+                f"Ontology class created successfully - "
+                f"class_id={ontology_class.class_id}"
+            )
+            
+            return ontology_class
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to create ontology class: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def get_by_id(self, class_id: UUID) -> Optional[OntologyClass]:
+        """根据ID获取类型
+        
+        Args:
+            class_id: 类型ID
+            
+        Returns:
+            Optional[OntologyClass]: 类型对象，不存在则返回None
+            
+        Examples:
+            >>> repo = OntologyClassRepository(db)
+            >>> ontology_class = repo.get_by_id(class_id)
+        """
+        try:
+            logger.debug(f"Getting ontology class by ID: {class_id}")
+            
+            ontology_class = self.db.query(OntologyClass).filter(
+                OntologyClass.class_id == class_id
+            ).first()
+            
+            if ontology_class:
+                logger.debug(f"Ontology class found: {class_id}")
+            else:
+                logger.debug(f"Ontology class not found: {class_id}")
+            
+            return ontology_class
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to get ontology class by ID: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def get_by_name(self, class_name: str, scene_id: UUID) -> Optional[OntologyClass]:
+        """根据类型名称和场景ID获取类型（精确匹配）
+        
+        Args:
+            class_name: 类型名称
+            scene_id: 场景ID
+            
+        Returns:
+            Optional[OntologyClass]: 类型对象，不存在则返回None
+            
+        Examples:
+            >>> repo = OntologyClassRepository(db)
+            >>> ontology_class = repo.get_by_name("患者", scene_id)
+        """
+        try:
+            logger.debug(f"Getting ontology class by name: {class_name}, scene_id: {scene_id}")
+            
+            ontology_class = self.db.query(OntologyClass).filter(
+                OntologyClass.class_name == class_name,
+                OntologyClass.scene_id == scene_id
+            ).first()
+            
+            if ontology_class:
+                logger.debug(f"Ontology class found: {class_name}")
+            else:
+                logger.debug(f"Ontology class not found: {class_name}")
+            
+            return ontology_class
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to get ontology class by name: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def search_by_name(self, keyword: str, scene_id: UUID) -> List[OntologyClass]:
+        """根据关键词模糊搜索类型
+        
+        使用 LIKE 进行模糊匹配，支持中文和英文。
+        
+        Args:
+            keyword: 搜索关键词
+            scene_id: 场景ID
+            
+        Returns:
+            List[OntologyClass]: 匹配的类型列表
+            
+        Examples:
+            >>> repo = OntologyClassRepository(db)
+            >>> classes = repo.search_by_name("患者", scene_id)
+        """
+        try:
+            logger.debug(
+                f"Searching ontology classes by keyword - "
+                f"keyword={keyword}, scene_id={scene_id}"
+            )
+            
+            # 使用 ilike 进行不区分大小写的模糊匹配
+            classes = self.db.query(OntologyClass).filter(
+                OntologyClass.class_name.ilike(f"%{keyword}%"),
+                OntologyClass.scene_id == scene_id
+            ).order_by(
+                OntologyClass.created_at.desc()
+            ).all()
+            
+            logger.info(
+                f"Found {len(classes)} ontology classes matching keyword '{keyword}' "
+                f"in scene {scene_id}"
+            )
+            
+            return classes
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to search ontology classes by keyword: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def get_by_scene(self, scene_id: UUID) -> List[OntologyClass]:
+        """获取场景下的所有类型
+        
+        按创建时间倒序排列。
+        
+        Args:
+            scene_id: 场景ID
+            
+        Returns:
+            List[OntologyClass]: 类型列表
+            
+        Examples:
+            >>> repo = OntologyClassRepository(db)
+            >>> classes = repo.get_by_scene(scene_id)
+        """
+        try:
+            logger.debug(f"Getting ontology classes by scene: {scene_id}")
+            
+            classes = self.db.query(OntologyClass).filter(
+                OntologyClass.scene_id == scene_id
+            ).order_by(
+                OntologyClass.created_at.desc()
+            ).all()
+            
+            logger.info(
+                f"Found {len(classes)} ontology classes in scene {scene_id}"
+            )
+            
+            return classes
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to get ontology classes by scene: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def update(self, class_id: UUID, update_data: dict) -> Optional[OntologyClass]:
+        """更新类型信息
+        
+        Args:
+            class_id: 类型ID
+            update_data: 更新数据字典
+            
+        Returns:
+            Optional[OntologyClass]: 更新后的类型对象，不存在则返回None
+            
+        Raises:
+            Exception: 数据库操作失败
+            
+        Examples:
+            >>> repo = OntologyClassRepository(db)
+            >>> ontology_class = repo.update(
+            ...     class_id,
+            ...     {"class_name": "新名称"}
+            ... )
+        """
+        try:
+            logger.info(f"Updating ontology class: {class_id}")
+            
+            ontology_class = self.get_by_id(class_id)
+            if not ontology_class:
+                logger.warning(f"Ontology class not found for update: {class_id}")
+                return None
+            
+            # 更新字段
+            if "class_name" in update_data and update_data["class_name"] is not None:
+                ontology_class.class_name = update_data["class_name"]
+            
+            if "class_description" in update_data:
+                ontology_class.class_description = update_data["class_description"]
+            
+            self.db.flush()
+            
+            logger.info(f"Ontology class updated successfully: {class_id}")
+            
+            return ontology_class
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to update ontology class: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def delete(self, class_id: UUID) -> bool:
+        """删除类型
+        
+        Args:
+            class_id: 类型ID
+            
+        Returns:
+            bool: 删除成功返回True，类型不存在返回False
+            
+        Raises:
+            Exception: 数据库操作失败
+            
+        Examples:
+            >>> repo = OntologyClassRepository(db)
+            >>> success = repo.delete(class_id)
+        """
+        try:
+            logger.info(f"Deleting ontology class: {class_id}")
+            
+            ontology_class = self.get_by_id(class_id)
+            if not ontology_class:
+                logger.warning(f"Ontology class not found for delete: {class_id}")
+                return False
+            
+            self.db.delete(ontology_class)
+            self.db.flush()
+            
+            logger.info(f"Ontology class deleted successfully: {class_id}")
+            
+            return True
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to delete ontology class: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def check_ownership(self, class_id: UUID, workspace_id: UUID) -> bool:
+        """检查类型是否属于指定工作空间（通过场景关联）
+        
+        Args:
+            class_id: 类型ID
+            workspace_id: 工作空间ID
+            
+        Returns:
+            bool: 属于返回True，否则返回False
+            
+        Examples:
+            >>> repo = OntologyClassRepository(db)
+            >>> is_owner = repo.check_ownership(class_id, workspace_id)
+        """
+        try:
+            logger.debug(
+                f"Checking class ownership - "
+                f"class_id={class_id}, workspace_id={workspace_id}"
+            )
+            
+            count = self.db.query(OntologyClass).join(
+                OntologyScene,
+                OntologyClass.scene_id == OntologyScene.scene_id
+            ).filter(
+                OntologyClass.class_id == class_id,
+                OntologyScene.workspace_id == workspace_id
+            ).count()
+            
+            is_owner = count > 0
+            
+            logger.debug(
+                f"Class ownership check result: {is_owner} - "
+                f"class_id={class_id}"
+            )
+            
+            return is_owner
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to check class ownership: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def get_scene_id_by_class(self, class_id: UUID) -> Optional[UUID]:
+        """根据类型ID获取所属场景ID
+        
+        Args:
+            class_id: 类型ID
+            
+        Returns:
+            Optional[UUID]: 场景ID，类型不存在则返回None
+            
+        Examples:
+            >>> repo = OntologyClassRepository(db)
+            >>> scene_id = repo.get_scene_id_by_class(class_id)
+        """
+        try:
+            logger.debug(f"Getting scene ID by class: {class_id}")
+            
+            ontology_class = self.get_by_id(class_id)
+            if not ontology_class:
+                logger.debug(f"Class not found: {class_id}")
+                return None
+            
+            logger.debug(
+                f"Found scene ID: {ontology_class.scene_id} for class: {class_id}"
+            )
+            
+            return ontology_class.scene_id
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to get scene ID by class: {str(e)}",
+                exc_info=True
+            )
+            raise

api/app/repositories/ontology_scene_repository.py

@@ -0,0 +1,394 @@
+# -*- coding: utf-8 -*-
+"""本体场景Repository层
+
+本模块提供本体场景的数据访问层实现。
+
+Classes:
+    OntologySceneRepository: 本体场景数据访问类
+"""
+
+import logging
+from typing import List, Optional
+from uuid import UUID
+
+from sqlalchemy.orm import Session, joinedload
+
+from app.core.logging_config import get_db_logger
+from app.models.ontology_scene import OntologyScene
+
+
+logger = get_db_logger()
+
+
+class OntologySceneRepository:
+    """本体场景Repository
+    
+    提供本体场景的CRUD操作和权限检查。
+    
+    Attributes:
+        db: SQLAlchemy数据库会话
+    """
+    
+    def __init__(self, db: Session):
+        """初始化Repository
+        
+        Args:
+            db: SQLAlchemy数据库会话
+        """
+        self.db = db
+    
+    def create(self, scene_data: dict, workspace_id: UUID) -> OntologyScene:
+        """创建本体场景
+        
+        Args:
+            scene_data: 场景数据字典，包含scene_name和scene_description
+            workspace_id: 所属工作空间ID
+            
+        Returns:
+            OntologyScene: 创建的场景对象
+            
+        Raises:
+            Exception: 数据库操作失败
+            
+        Examples:
+            >>> repo = OntologySceneRepository(db)
+            >>> scene = repo.create(
+            ...     {"scene_name": "医疗场景", "scene_description": "描述"},
+            ...     workspace_id
+            ... )
+        """
+        try:
+            logger.info(
+                f"Creating ontology scene - "
+                f"name={scene_data.get('scene_name')}, "
+                f"workspace_id={workspace_id}"
+            )
+            
+            scene = OntologyScene(
+                scene_name=scene_data.get("scene_name"),
+                scene_description=scene_data.get("scene_description"),
+                workspace_id=workspace_id
+            )
+            
+            self.db.add(scene)
+            self.db.flush()  # 获取ID但不提交
+            
+            logger.info(
+                f"Ontology scene created successfully - "
+                f"scene_id={scene.scene_id}"
+            )
+            
+            return scene
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to create ontology scene: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def get_by_id(self, scene_id: UUID) -> Optional[OntologyScene]:
+        """根据ID获取场景
+        
+        Args:
+            scene_id: 场景ID
+            
+        Returns:
+            Optional[OntologyScene]: 场景对象，不存在则返回None
+            
+        Examples:
+            >>> repo = OntologySceneRepository(db)
+            >>> scene = repo.get_by_id(scene_id)
+        """
+        try:
+            logger.debug(f"Getting ontology scene by ID: {scene_id}")
+            
+            scene = self.db.query(OntologyScene).filter(
+                OntologyScene.scene_id == scene_id
+            ).first()
+            
+            if scene:
+                logger.debug(f"Ontology scene found: {scene_id}")
+            else:
+                logger.debug(f"Ontology scene not found: {scene_id}")
+            
+            return scene
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to get ontology scene by ID: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def get_by_name(self, scene_name: str, workspace_id: UUID) -> Optional[OntologyScene]:
+        """根据场景名称和工作空间ID获取场景（精确匹配）
+        
+        Args:
+            scene_name: 场景名称
+            workspace_id: 工作空间ID
+            
+        Returns:
+            Optional[OntologyScene]: 场景对象，不存在则返回None
+            
+        Examples:
+            >>> repo = OntologySceneRepository(db)
+            >>> scene = repo.get_by_name("医疗场景", workspace_id)
+        """
+        try:
+            logger.debug(
+                f"Getting ontology scene by name - "
+                f"scene_name={scene_name}, workspace_id={workspace_id}"
+            )
+            
+            scene = self.db.query(OntologyScene).options(
+                joinedload(OntologyScene.classes)
+            ).filter(
+                OntologyScene.scene_name == scene_name,
+                OntologyScene.workspace_id == workspace_id
+            ).first()
+            
+            if scene:
+                logger.debug(f"Ontology scene found: {scene_name}")
+            else:
+                logger.debug(f"Ontology scene not found: {scene_name}")
+            
+            return scene
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to get ontology scene by name: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def search_by_name(self, keyword: str, workspace_id: UUID) -> List[OntologyScene]:
+        """根据关键词模糊搜索场景
+        
+        使用 LIKE 进行模糊匹配，支持中文和英文。
+        
+        Args:
+            keyword: 搜索关键词
+            workspace_id: 工作空间ID
+            
+        Returns:
+            List[OntologyScene]: 匹配的场景列表
+            
+        Examples:
+            >>> repo = OntologySceneRepository(db)
+            >>> scenes = repo.search_by_name("医疗", workspace_id)
+        """
+        try:
+            logger.debug(
+                f"Searching ontology scenes by keyword - "
+                f"keyword={keyword}, workspace_id={workspace_id}"
+            )
+            
+            # 使用 ilike 进行不区分大小写的模糊匹配
+            scenes = self.db.query(OntologyScene).options(
+                joinedload(OntologyScene.classes)
+            ).filter(
+                OntologyScene.scene_name.ilike(f"%{keyword}%"),
+                OntologyScene.workspace_id == workspace_id
+            ).order_by(
+                OntologyScene.updated_at.desc()
+            ).all()
+            
+            logger.info(
+                f"Found {len(scenes)} ontology scenes matching keyword '{keyword}' "
+                f"in workspace {workspace_id}"
+            )
+            
+            return scenes
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to search ontology scenes by keyword: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def get_by_workspace(self, workspace_id: UUID, page: Optional[int] = None, page_size: Optional[int] = None) -> tuple:
+        """获取工作空间下的所有场景（支持分页）
+        
+        使用joinedload预加载classes关系以统计数量。
+        
+        Args:
+            workspace_id: 工作空间ID
+            page: 页码（可选，从1开始）
+            page_size: 每页数量（可选）
+            
+        Returns:
+            tuple: (场景列表, 总数量)
+            
+        Examples:
+            >>> repo = OntologySceneRepository(db)
+            >>> scenes, total = repo.get_by_workspace(workspace_id)
+            >>> scenes, total = repo.get_by_workspace(workspace_id, page=1, page_size=10)
+        """
+        try:
+            logger.debug(f"Getting ontology scenes by workspace: {workspace_id}, page={page}, page_size={page_size}")
+            
+            # 构建基础查询
+            query = self.db.query(OntologyScene).options(
+                joinedload(OntologyScene.classes)
+            ).filter(
+                OntologyScene.workspace_id == workspace_id
+            ).order_by(
+                OntologyScene.updated_at.desc()
+            )
+            
+            # 获取总数
+            total = query.count()
+            
+            # 如果提供了分页参数，应用分页
+            if page is not None and page_size is not None:
+                offset = (page - 1) * page_size
+                query = query.offset(offset).limit(page_size)
+                logger.debug(f"Applying pagination: offset={offset}, limit={page_size}")
+            
+            scenes = query.all()
+            
+            logger.info(
+                f"Found {len(scenes)} ontology scenes (total: {total}) in workspace {workspace_id}"
+            )
+            
+            return scenes, total
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to get ontology scenes by workspace: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def update(self, scene_id: UUID, update_data: dict) -> Optional[OntologyScene]:
+        """更新场景信息
+        
+        Args:
+            scene_id: 场景ID
+            update_data: 更新数据字典
+            
+        Returns:
+            Optional[OntologyScene]: 更新后的场景对象，不存在则返回None
+            
+        Raises:
+            Exception: 数据库操作失败
+            
+        Examples:
+            >>> repo = OntologySceneRepository(db)
+            >>> scene = repo.update(
+            ...     scene_id,
+            ...     {"scene_name": "新名称"}
+            ... )
+        """
+        try:
+            logger.info(f"Updating ontology scene: {scene_id}")
+            
+            scene = self.get_by_id(scene_id)
+            if not scene:
+                logger.warning(f"Ontology scene not found for update: {scene_id}")
+                return None
+            
+            # 更新字段
+            if "scene_name" in update_data and update_data["scene_name"] is not None:
+                scene.scene_name = update_data["scene_name"]
+            
+            if "scene_description" in update_data:
+                scene.scene_description = update_data["scene_description"]
+            
+            self.db.flush()
+            
+            logger.info(f"Ontology scene updated successfully: {scene_id}")
+            
+            return scene
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to update ontology scene: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def delete(self, scene_id: UUID) -> bool:
+        """删除场景（级联删除类型）
+        
+        依赖数据库级联删除配置（ondelete="CASCADE"）。
+        
+        Args:
+            scene_id: 场景ID
+            
+        Returns:
+            bool: 删除成功返回True，场景不存在返回False
+            
+        Raises:
+            Exception: 数据库操作失败
+            
+        Examples:
+            >>> repo = OntologySceneRepository(db)
+            >>> success = repo.delete(scene_id)
+        """
+        try:
+            logger.info(f"Deleting ontology scene: {scene_id}")
+            
+            scene = self.get_by_id(scene_id)
+            if not scene:
+                logger.warning(f"Ontology scene not found for delete: {scene_id}")
+                return False
+            
+            self.db.delete(scene)
+            self.db.flush()
+            
+            logger.info(
+                f"Ontology scene deleted successfully (cascade): {scene_id}"
+            )
+            
+            return True
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to delete ontology scene: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def check_ownership(self, scene_id: UUID, workspace_id: UUID) -> bool:
+        """检查场景是否属于指定工作空间
+        
+        Args:
+            scene_id: 场景ID
+            workspace_id: 工作空间ID
+            
+        Returns:
+            bool: 属于返回True，否则返回False
+            
+        Examples:
+            >>> repo = OntologySceneRepository(db)
+            >>> is_owner = repo.check_ownership(scene_id, workspace_id)
+        """
+        try:
+            logger.debug(
+                f"Checking scene ownership - "
+                f"scene_id={scene_id}, workspace_id={workspace_id}"
+            )
+            
+            count = self.db.query(OntologyScene).filter(
+                OntologyScene.scene_id == scene_id,
+                OntologyScene.workspace_id == workspace_id
+            ).count()
+            
+            is_owner = count > 0
+            
+            logger.debug(
+                f"Scene ownership check result: {is_owner} - "
+                f"scene_id={scene_id}"
+            )
+            
+            return is_owner
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to check scene ownership: {str(e)}",
+                exc_info=True
+            )
+            raise

api/app/schemas/ontology_schemas.py

@@ -0,0 +1,461 @@
+"""本体提取API的请求和响应模型
+
+本模块定义了本体提取系统的所有API请求和响应的Pydantic模型。
+
+Classes:
+    ExtractionRequest: 本体提取请求模型
+    ExtractionResponse: 本体提取响应模型
+    ExportRequest: OWL文件导出请求模型
+    ExportResponse: OWL文件导出响应模型
+    OntologyResultResponse: 本体提取结果响应模型(带毫秒时间戳)
+    SceneCreateRequest: 场景创建请求模型
+    SceneUpdateRequest: 场景更新请求模型
+    SceneResponse: 场景响应模型
+    SceneListResponse: 场景列表响应模型
+    ClassCreateRequest: 类型创建请求模型
+    ClassUpdateRequest: 类型更新请求模型
+    ClassResponse: 类型响应模型
+    ClassListResponse: 类型列表响应模型
+"""
+
+from typing import List, Optional
+import datetime
+from uuid import UUID
+
+from pydantic import BaseModel, Field, field_serializer, ConfigDict
+
+from app.core.memory.models.ontology_models import OntologyClass
+
+
+class ExtractionRequest(BaseModel):
+    """本体提取请求模型
+    
+    用于POST /api/ontology/extract端点的请求体。
+    
+    Attributes:
+        scenario: 场景描述文本,不能为空
+        domain: 可选的领域提示(如Healthcare, Education等)
+        llm_id: LLM模型ID,必须提供
+        scene_id: 场景ID,必须提供,用于将提取的类保存到指定场景
+    
+    Examples:
+        >>> request = ExtractionRequest(
+        ...     scenario="医院管理患者记录...",
+        ...     domain="Healthcare",
+        ...     llm_id="550e8400-e29b-41d4-a716-446655440000",
+        ...     scene_id="660e8400-e29b-41d4-a716-446655440000"
+        ... )
+    """
+    scenario: str = Field(..., description="场景描述文本", min_length=1)
+    domain: Optional[str] = Field(None, description="可选的领域提示")
+    llm_id: str = Field(..., description="LLM模型ID")
+    scene_id: UUID = Field(..., description="场景ID,用于将提取的类保存到指定场景")
+
+
+class ExtractionResponse(BaseModel):
+    """本体提取响应模型
+    
+    用于POST /api/ontology/extract端点的响应体。
+    
+    Attributes:
+        classes: 提取的本体类列表
+        domain: 识别的领域
+        extracted_count: 提取的类数量
+    
+    Examples:
+        >>> response = ExtractionResponse(
+        ...     classes=[...],
+        ...     domain="Healthcare",
+        ...     extracted_count=7
+        ... )
+    """
+    classes: List[OntologyClass] = Field(default_factory=list, description="提取的本体类列表")
+    domain: str = Field(..., description="识别的领域")
+    extracted_count: int = Field(..., description="提取的类数量")
+
+
+class ExportRequest(BaseModel):
+    """OWL文件导出请求模型
+    
+    用于POST /api/ontology/export端点的请求体。
+    
+    Attributes:
+        classes: 要导出的本体类列表
+        format: 导出格式,可选值: rdfxml, turtle, ntriples, json
+        include_metadata: 是否包含完整的OWL元数据(命名空间等),默认True
+    
+    Examples:
+        >>> request = ExportRequest(
+        ...     classes=[...],
+        ...     format="rdfxml",
+        ...     include_metadata=True
+        ... )
+    """
+    classes: List[OntologyClass] = Field(..., description="要导出的本体类列表", min_length=1)
+    format: str = Field("rdfxml", description="导出格式: rdfxml, turtle, ntriples, json")
+    include_metadata: bool = Field(True, description="是否包含完整的OWL元数据")
+
+
+class ExportResponse(BaseModel):
+    """OWL文件导出响应模型
+    
+    用于POST /api/ontology/export端点的响应体。
+    
+    Attributes:
+        owl_content: OWL文件内容
+        format: 导出格式
+        classes_count: 导出的类数量
+    
+    Examples:
+        >>> response = ExportResponse(
+        ...     owl_content="<?xml version='1.0'?>...",
+        ...     format="rdfxml",
+        ...     classes_count=7
+        ... )
+    """
+    owl_content: str = Field(..., description="OWL文件内容")
+    format: str = Field(..., description="导出格式")
+    classes_count: int = Field(..., description="导出的类数量")
+
+
+class OntologyResultResponse(BaseModel):
+    """本体提取结果响应模型
+    
+    用于返回数据库中存储的提取结果,时间戳为毫秒级。
+    
+    Attributes:
+        id: 结果ID (UUID)
+        scenario: 场景描述文本
+        domain: 领域
+        classes_json: 提取的本体类数据(JSON格式)
+        extracted_count: 提取的类数量
+        user_id: 用户ID
+        created_at: 创建时间(毫秒时间戳)
+    
+    Examples:
+        >>> response = OntologyResultResponse(
+        ...     id=uuid.uuid4(),
+        ...     scenario="医院管理患者记录...",
+        ...     domain="Healthcare",
+        ...     classes_json={"classes": [...]},
+        ...     extracted_count=7,
+        ...     user_id=123,
+        ...     created_at=datetime.now()
+        ... )
+    """
+    id: UUID = Field(..., description="结果ID")
+    scenario: str = Field(..., description="场景描述文本")
+    domain: Optional[str] = Field(None, description="领域")
+    classes_json: dict = Field(..., description="提取的本体类数据(JSON格式)")
+    extracted_count: int = Field(..., description="提取的类数量")
+    user_id: Optional[int] = Field(None, description="用户ID")
+    created_at: datetime.datetime = Field(..., description="创建时间")
+    
+    @field_serializer("created_at", when_used="json")
+    def _serialize_created_at(self, dt: datetime.datetime):
+        """将创建时间序列化为毫秒时间戳"""
+        return int(dt.timestamp() * 1000) if dt else None
+    
+    class Config:
+        from_attributes = True
+
+
+
+# ==================== 本体场景相关 Schema ====================
+
+class SceneCreateRequest(BaseModel):
+    """场景创建请求模型
+    
+    用于创建新的本体场景。
+    
+    Attributes:
+        scene_name: 场景名称，必填，1-200字符
+        scene_description: 场景描述，可选
+    
+    Examples:
+        >>> request = SceneCreateRequest(
+        ...     scene_name="医疗场景",
+        ...     scene_description="用于医疗领域的本体建模"
+        ... )
+    """
+    scene_name: str = Field(..., min_length=1, max_length=200, description="场景名称")
+    scene_description: Optional[str] = Field(None, description="场景描述")
+
+
+class SceneUpdateRequest(BaseModel):
+    """场景更新请求模型
+    
+    用于更新已有本体场景信息。
+    
+    Attributes:
+        scene_name: 场景名称，可选，1-200字符
+        scene_description: 场景描述，可选
+    
+    Examples:
+        >>> request = SceneUpdateRequest(
+        ...     scene_name="更新后的场景名称",
+        ...     scene_description="更新后的描述"
+        ... )
+    """
+    scene_name: Optional[str] = Field(None, min_length=1, max_length=200, description="场景名称")
+    scene_description: Optional[str] = Field(None, description="场景描述")
+
+
+class SceneResponse(BaseModel):
+    """场景响应模型
+    
+    用于返回本体场景信息。
+    
+    Attributes:
+        scene_id: 场景ID
+        scene_name: 场景名称
+        scene_description: 场景描述
+        type_num: 类型数量
+        workspace_id: 所属工作空间ID
+        created_at: 创建时间（毫秒时间戳）
+        updated_at: 更新时间（毫秒时间戳）
+        classes_count: 类型数量
+    
+    Examples:
+        >>> response = SceneResponse(
+        ...     scene_id=uuid.uuid4(),
+        ...     scene_name="医疗场景",
+        ...     scene_description="用于医疗领域的本体建模",
+        ...     type_num=0,
+        ...     workspace_id=uuid.uuid4(),
+        ...     created_at=datetime.now(),
+        ...     updated_at=datetime.now(),
+        ...     classes_count=5
+        ... )
+    """
+    scene_id: UUID = Field(..., description="场景ID")
+    scene_name: str = Field(..., description="场景名称")
+    scene_description: Optional[str] = Field(None, description="场景描述")
+    type_num: int = Field(..., description="类型数量")
+    entity_type: Optional[List[str]] = Field(None, description="实体类型列表（最多3个class_name）")
+    workspace_id: UUID = Field(..., description="所属工作空间ID")
+    created_at: datetime.datetime = Field(..., description="创建时间（毫秒时间戳）")
+    updated_at: datetime.datetime = Field(..., description="更新时间（毫秒时间戳）")
+    classes_count: int = Field(0, description="类型数量")
+    
+    @field_serializer("created_at", when_used="json")
+    def _serialize_created_at(self, dt: datetime.datetime):
+        """将创建时间序列化为毫秒时间戳"""
+        return int(dt.timestamp() * 1000) if dt else None
+    
+    @field_serializer("updated_at", when_used="json")
+    def _serialize_updated_at(self, dt: datetime.datetime):
+        """将更新时间序列化为毫秒时间戳"""
+        return int(dt.timestamp() * 1000) if dt else None
+    
+    model_config = ConfigDict(from_attributes=True)
+
+
+class PaginationInfo(BaseModel):
+    """分页信息模型
+    
+    Attributes:
+        page: 当前页码
+        pagesize: 每页数量
+        total: 总数量
+        hasnext: 是否有下一页
+    """
+    page: int = Field(..., description="当前页码")
+    pagesize: int = Field(..., description="每页数量")
+    total: int = Field(..., description="总数量")
+    hasnext: bool = Field(..., description="是否有下一页")
+
+
+class SceneListResponse(BaseModel):
+    """场景列表响应模型（支持分页）
+    
+    用于返回本体场景列表。
+    
+    Attributes:
+        items: 场景列表
+        page: 分页信息（可选，分页时返回）
+    
+    Examples:
+        >>> # 不分页
+        >>> response = SceneListResponse(
+        ...     items=[scene1, scene2]
+        ... )
+        >>> # 分页
+        >>> response = SceneListResponse(
+        ...     items=[scene1, scene2, ...],
+        ...     page=PaginationInfo(page=1, pagesize=100, total=150, hasnext=True)
+        ... )
+    """
+    items: List[SceneResponse] = Field(..., description="场景列表")
+    page: Optional[PaginationInfo] = Field(None, description="分页信息")
+
+
+# ==================== 本体类型相关 Schema ====================
+
+class ClassItem(BaseModel):
+    """单个类型信息模型
+    
+    Attributes:
+        class_name: 类型名称，必填，1-200字符
+        class_description: 类型描述，可选
+    
+    Examples:
+        >>> item = ClassItem(
+        ...     class_name="患者",
+        ...     class_description="医院患者信息"
+        ... )
+    """
+    class_name: str = Field(..., min_length=1, max_length=200, description="类型名称")
+    class_description: Optional[str] = Field(None, description="类型描述")
+
+
+class ClassCreateRequest(BaseModel):
+    """类型创建请求模型（统一使用列表形式）
+    
+    通过列表中元素数量决定创建模式：
+    - 列表包含 1 个元素：单个创建
+    - 列表包含多个元素：批量创建
+    
+    Attributes:
+        scene_id: 所属场景ID，必填
+        classes: 类型列表，必填，至少包含 1 个元素
+    
+    Examples:
+        # 单个创建（列表中 1 个元素）
+        >>> request = ClassCreateRequest(
+        ...     scene_id=uuid.uuid4(),
+        ...     classes=[
+        ...         ClassItem(class_name="患者", class_description="医院患者信息")
+        ...     ]
+        ... )
+        
+        # 批量创建（列表中多个元素）
+        >>> request = ClassCreateRequest(
+        ...     scene_id=uuid.uuid4(),
+        ...     classes=[
+        ...         ClassItem(class_name="患者", class_description="医院患者信息"),
+        ...         ClassItem(class_name="医生", class_description="医院医生信息"),
+        ...         ClassItem(class_name="药品", class_description="医院药品信息")
+        ...     ]
+        ... )
+    """
+    scene_id: UUID = Field(..., description="所属场景ID")
+    classes: List[ClassItem] = Field(..., min_length=1, description="类型列表，至少包含 1 个元素")
+
+
+class ClassUpdateRequest(BaseModel):
+    """类型更新请求模型
+    
+    用于更新已有本体类型信息。
+    
+    Attributes:
+        class_name: 类型名称，可选，1-200字符
+        class_description: 类型描述，可选
+    
+    Examples:
+        >>> request = ClassUpdateRequest(
+        ...     class_name="更新后的类型名称",
+        ...     class_description="更新后的描述"
+        ... )
+    """
+    class_name: Optional[str] = Field(None, min_length=1, max_length=200, description="类型名称")
+    class_description: Optional[str] = Field(None, description="类型描述")
+
+
+class ClassResponse(BaseModel):
+    """类型响应模型
+    
+    用于返回本体类型信息。
+    
+    Attributes:
+        class_id: 类型ID
+        class_name: 类型名称
+        class_description: 类型描述
+        scene_id: 所属场景ID
+        created_at: 创建时间（毫秒时间戳）
+        updated_at: 更新时间（毫秒时间戳）
+    
+    Examples:
+        >>> response = ClassResponse(
+        ...     class_id=uuid.uuid4(),
+        ...     class_name="患者",
+        ...     class_description="医院患者信息",
+        ...     scene_id=uuid.uuid4(),
+        ...     created_at=datetime.now(),
+        ...     updated_at=datetime.now()
+        ... )
+    """
+    class_id: UUID = Field(..., description="类型ID")
+    class_name: str = Field(..., description="类型名称")
+    class_description: Optional[str] = Field(None, description="类型描述")
+    scene_id: UUID = Field(..., description="所属场景ID")
+    created_at: datetime.datetime = Field(..., description="创建时间（毫秒时间戳）")
+    updated_at: datetime.datetime = Field(..., description="更新时间（毫秒时间戳）")
+    
+    @field_serializer("created_at", when_used="json")
+    def _serialize_created_at(self, dt: datetime.datetime):
+        """将创建时间序列化为毫秒时间戳"""
+        return int(dt.timestamp() * 1000) if dt else None
+    
+    @field_serializer("updated_at", when_used="json")
+    def _serialize_updated_at(self, dt: datetime.datetime):
+        """将更新时间序列化为毫秒时间戳"""
+        return int(dt.timestamp() * 1000) if dt else None
+    
+    model_config = ConfigDict(from_attributes=True)
+
+
+class ClassBatchCreateResponse(BaseModel):
+    """批量创建类型响应模型
+    
+    用于返回批量创建的结果统计和详情。
+    
+    Attributes:
+        total: 总共尝试创建的数量
+        success_count: 成功创建的数量
+        failed_count: 失败的数量
+        items: 成功创建的类型列表
+        errors: 失败的错误信息列表（可选）
+    
+    Examples:
+        >>> response = ClassBatchCreateResponse(
+        ...     total=3,
+        ...     success_count=2,
+        ...     failed_count=1,
+        ...     items=[class1, class2],
+        ...     errors=["创建类型 '药品' 失败: 类型名称已存在"]
+        ... )
+    """
+    total: int = Field(..., description="总共尝试创建的数量")
+    success_count: int = Field(..., description="成功创建的数量")
+    failed_count: int = Field(0, description="失败的数量")
+    items: List[ClassResponse] = Field(..., description="成功创建的类型列表")
+    errors: Optional[List[str]] = Field(None, description="失败的错误信息列表")
+
+
+class ClassListResponse(BaseModel):
+    """类型列表响应模型
+    
+    用于返回本体类型列表。
+    
+    Attributes:
+        total: 总数量
+        scene_id: 所属场景ID
+        scene_name: 场景名称
+        scene_description: 场景描述
+        items: 类型列表
+    
+    Examples:
+        >>> response = ClassListResponse(
+        ...     total=3,
+        ...     scene_id=uuid.uuid4(),
+        ...     scene_name="医疗场景",
+        ...     scene_description="用于医疗领域的本体建模",
+        ...     items=[class1, class2, class3]
+        ... )
+    """
+    total: int = Field(..., description="总数量")
+    scene_id: UUID = Field(..., description="所属场景ID")
+    scene_name: str = Field(..., description="场景名称")
+    scene_description: Optional[str] = Field(None, description="场景描述")
+    items: List[ClassResponse] = Field(..., description="类型列表")

api/pyproject.toml

@@ -140,6 +140,7 @@ dependencies = [
     "oss2>=2.19.1",
     "flower>=2.0.1",
     "aiofiles>=23.0.0",
+    "owlready2>=0.46",
 ]
 
 [tool.pytest.ini_options]