feat: Add curated expert occupations with local data sources

- Add curated occupations seed files (210 entries in zh/en) with specific domains
- Add DBpedia occupations data (2164 entries) for external source option
- Refactor expert_source_service to read from local JSON files
- Improve keyword generation prompts to leverage expert domain context
- Add architecture analysis documentation (ARCHITECTURE_ANALYSIS.md)
- Fix expert source selection bug (proper handling of empty custom_experts)
- Update frontend to support curated/dbpedia/wikidata expert sources

Key changes:
- backend/app/data/: Local occupation data files
- backend/app/services/expert_source_service.py: Simplified local file reading
- backend/app/prompts/expert_transformation_prompt.py: Better domain-aware prompts
- Removed expert_cache.py (no longer needed with local files)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

ARCHITECTURE_ANALYSIS.md

@@ -0,0 +1,277 @@
+# novelty-seeking 系統流程與耦合度分析
+
+> 生成日期: 2025-12-04
+
+## 一、系統整體架構概覽
+
+novelty-seeking 是一個創新思維引導系統，由三個核心 Agent 組成：
+- **Attribute Agent**：從查詢到屬性節點的映射
+- **Transformation Agent**：屬性到新關鍵字的轉換
+- **Expert Transformation Agent**：多視角專家角度的屬性轉換
+
+---
+
+## 二、完整資料流程
+
+```
+┌─────────────────────────────────────────────────────────────────────┐
+│                        Attribute Agent                               │
+├─────────────────────────────────────────────────────────────────────┤
+│  用戶輸入 Query (如「腳踏車」)                                        │
+│       ↓                                                              │
+│  Step 0: 類別分析 (category_mode 決定)                               │
+│       → 產出: CategoryDefinition[] (如 材料/功能/用途/使用族群)        │
+│       ↓                                                              │
+│  Step 1: 屬性列表生成                                                │
+│       → 產出: {材料: [鋼,木,碳纖維], 功能: [搬運,儲存], ...}           │
+│       ↓                                                              │
+│  Step 2: 關係生成 (DAG 邊)                                           │
+│       → 產出: AttributeDAG (nodes + edges)                           │
+└─────────────────────────────────────────────────────────────────────┘
+                              ↓ (高耦合)
+┌─────────────────────────────────────────────────────────────────────┐
+│                   Expert Transformation Agent                        │
+├─────────────────────────────────────────────────────────────────────┤
+│  輸入: Query + Category + Attributes (來自 Attribute Agent)          │
+│       ↓                                                              │
+│  Step 0: 專家團隊生成                                                │
+│       → expert_source 決定: llm / curated / dbpedia / wikidata       │
+│       → 產出: ExpertProfile[] (如 會計師/心理師/生態學家)              │
+│       ↓                                                              │
+│  Step 1: 專家視角關鍵字生成 (對每個 attribute)                        │
+│       → 產出: ExpertKeyword[] (關鍵字 + 來源專家 + 來源屬性)          │
+│       ↓                                                              │
+│  Step 2: 描述生成 (對每個 keyword)                                   │
+│       → 產出: ExpertTransformationDescription[]                      │
+└─────────────────────────────────────────────────────────────────────┘
+```
+
+---
+
+## 三、Attribute Agent 詳細流程
+
+### 3.1 流程架構
+
+```
+用戶查詢 (Query)
+    ↓
+┌─────────────────────────────────────────────┐
+│ Step 0: 類別分析 (Category Mode 決定)      │
+├─────────────────────────────────────────────┤
+│ 輸入: query, suggested_category_count      │
+│ 處理:                                        │
+│ - FIXED_ONLY: 使用 4 個固定類別              │
+│ - FIXED_PLUS_CUSTOM: 固定 + 用戶自訂        │
+│ - FIXED_PLUS_DYNAMIC: 固定 + LLM 推薦       │
+│ - CUSTOM_ONLY: 僅 LLM 推薦                  │
+│ - DYNAMIC_AUTO: 純 LLM 推薦 (預設)          │
+│ 輸出: Step0Result (recommended categories)  │
+└─────────────────────────────────────────────┘
+    ↓
+┌─────────────────────────────────────────────┐
+│ Step 1: 屬性列表生成 (Attributes)          │
+├─────────────────────────────────────────────┤
+│ 輸入: query, final_categories              │
+│ LLM 處理:                                    │
+│ - 分析 query 在各類別下的屬性               │
+│ - 每個類別生成 3-5 個屬性                    │
+│ 輸出: DynamicStep1Result                    │
+└─────────────────────────────────────────────┘
+    ↓
+┌─────────────────────────────────────────────┐
+│ Step 2: 關係映射 (Relationships → DAG)     │
+├─────────────────────────────────────────────┤
+│ 輸入: query, categories, attributes        │
+│ LLM 處理:                                    │
+│ - 分析相鄰類別之間的因果關係                │
+│ - 生成 (source, target) 關係對               │
+│ 輸出: AttributeDAG                          │
+└─────────────────────────────────────────────┘
+```
+
+### 3.2 關鍵輸入變數
+
+| 變數 | 來源 | 影響範圍 | 作用 |
+|------|------|--------|------|
+| `query` | 用戶輸入 | Step 0-2 全部 | 決定分析的物件 |
+| `category_mode` | 用戶選擇 | Step 0 | 決定使用哪些類別 |
+| `suggested_category_count` | 用戶設定 | Step 0 | LLM 推薦類別的數量 |
+| `temperature` | 用戶設定 | Step 0-2 | 控制 LLM 輸出的多樣性 |
+| `model` | 用戶選擇 | Step 0-2 | 選擇不同的 LLM 模型 |
+
+---
+
+## 四、Expert Transformation Agent 詳細流程
+
+### 4.1 流程架構
+
+```
+屬性列表 (attributes from Attribute Agent)
+    ↓
+┌─────────────────────────────────────────────┐
+│ Step 0: 專家團隊生成 (Expert Generation)   │
+├─────────────────────────────────────────────┤
+│ 決定因素:                                    │
+│ - expert_source = 'llm' → LLM 生成          │
+│ - expert_source ∈ ['curated', 'dbpedia',   │
+│   'wikidata'] → 本地檔案隨機選取             │
+│ - 有 custom_experts → 結合 LLM              │
+│                                             │
+│ 輸出: ExpertProfile[]                       │
+│       [{id, name, domain, perspective}]    │
+└─────────────────────────────────────────────┘
+    ↓
+┌─────────────────────────────────────────────┐
+│ Step 1: 專家視角關鍵字生成 (Keywords)       │
+├─────────────────────────────────────────────┤
+│ 迴圈: for each attribute in attributes:    │
+│   LLM 為每個專家生成 keywords_per_expert    │
+│   個關鍵字                                   │
+│                                             │
+│ 輸出: ExpertKeyword[]                       │
+│       [{keyword, expert_id, expert_name,    │
+│         source_attribute}]                  │
+└─────────────────────────────────────────────┘
+    ↓
+┌─────────────────────────────────────────────┐
+│ Step 2: 描述生成 (Descriptions)             │
+├─────────────────────────────────────────────┤
+│ 迴圈: for each expert_keyword:              │
+│   LLM 生成 15-30 字的創新應用描述           │
+│                                             │
+│ 輸出: ExpertTransformationDescription[]     │
+└─────────────────────────────────────────────┘
+```
+
+### 4.2 關鍵輸入變數
+
+| 變數 | 來源 | 影響範圍 | 作用 |
+|------|------|--------|------|
+| `expert_source` | 用戶選擇 | Step 0 | 決定專家來源 (llm/curated/dbpedia/wikidata) |
+| `expert_count` | 用戶設定 | Step 0 | 專家數量 (2-8) |
+| `keywords_per_expert` | 用戶設定 | Step 1 | 每專家每屬性關鍵字數 (1-3) |
+| `custom_experts` | 用戶輸入 | Step 0 | 用戶指定的專家名稱 |
+| `temperature` | 用戶設定 | Step 0-2 | 控制多樣性 |
+
+### 4.3 關鍵字生成公式
+
+```
+總關鍵字數 = len(attributes) × expert_count × keywords_per_expert
+
+範例計算:
+├─ 3 個屬性 (搬運, 儲存, 展示)
+├─ 3 位專家 (會計師, 心理師, 生態學家)
+├─ 1 個關鍵字/專家
+└─ = 3 × 3 × 1 = 9 個關鍵字
+```
+
+---
+
+## 五、關鍵字生成影響因素
+
+| 階段 | 影響變數 | 影響程度 | 說明 |
+|------|---------|---------|------|
+| **屬性生成** | `query` | 極高 | 決定 LLM 分析的語義基礎 |
+| | `category_mode` | 高 | 決定類別維度 |
+| | `temperature` | 中 | 越高越多樣 |
+| | `model` | 中 | 不同模型知識基礎不同 |
+| **專家生成** | `expert_source` | 高 | 決定專家來源與品質 |
+| | `expert_count` | 中 | 2-8 位專家 |
+| | `custom_experts` | 中 | 與 LLM 結合 |
+| **關鍵字生成** | `experts[].domain` | 極高 | 直接決定關鍵字視角 |
+| | `keywords_per_expert` | 低 | 控制數量 |
+| | `source_attribute` | 高 | 決定思考起點 |
+
+---
+
+## 六、耦合度分析
+
+### 6.1 高耦合連接 ⚠️
+
+| 連接 | 耦合度 | 原因 | 風險 |
+|------|--------|------|------|
+| Attribute → Expert Transform | 高 | Expert 依賴 Attribute 輸出 | 結構變更需同步修改 |
+| Expert 生成 → Keyword 生成 | 高 | domain 直接影響關鍵字 | domain 品質差→關鍵字無關 |
+| Prompt → LLM 輸出結構 | 高 | prompt 定義 JSON 格式 | 改 prompt 需改 schema |
+
+### 6.2 低耦合連接 ✓
+
+| 連接 | 耦合度 | 原因 | 優點 |
+|------|--------|------|------|
+| curated/dbpedia/wikidata | 低 | 獨立本地檔案 | 可單獨更新 |
+| SSE 通信格式 | 低 | 標準化解耦 | 向後相容 |
+| useAttribute/useExpertTransformation | 低 | 獨立 hook | 可單獨複用 |
+
+### 6.3 耦合度矩陣
+
+|  | Attribute | Transformation | Expert Transform |
+|----|-----------|---------------|----|
+| **Attribute** | - | 低 | 高 |
+| **Transformation** | 低 | - | 低 |
+| **Expert Transform** | 高 | 低 | - |
+
+---
+
+## 七、專家來源比較
+
+| 來源 | 檔案 | 筆數 | Domain 品質 | 特點 |
+|------|------|------|------------|------|
+| `llm` | - | 動態 | 高 | LLM 根據 query 生成相關專家 |
+| `curated` | curated_occupations_zh/en.json | 210 | 高 | 精選職業，含具體領域 |
+| `dbpedia` | dbpedia_occupations_en.json | 2164 | 低 | 全是 "Professional Field" |
+| `wikidata` | - | - | - | 未實作本地化 |
+
+---
+
+## 八、決策變化追蹤範例
+
+```
+Query: "腳踏車"
+    ↓
+Category Mode: DYNAMIC_AUTO
+    → LLM 建議 [材料, 功能, 用途, 使用族群]
+    ↓
+Expert Source: "curated"
+    → 隨機選取 [外科醫師(醫療與健康), 軟體工程師(資訊科技), 主廚(餐飲與服務)]
+    ↓
+Attribute "搬運" + Expert "外科醫師"
+    → LLM 思考: 醫療視角看搬運
+    → Keyword: "器官運輸", "急救物流"
+    ↓
+Description 生成:
+    → "從急救醫療角度，腳踏車可改良為緊急醫療運輸工具..."
+```
+
+---
+
+## 九、改進建議
+
+| 問題 | 現狀 | 建議改進 |
+|------|------|---------|
+| domain 品質 | DBpedia 全是通用值 | ✅ 已建立精選職業 |
+| 重複計算 Expert | 每類別重新生成 | 考慮 Expert 全局化 |
+| Temperature 統一 | 整流程同一值 | 可按 Step 分開設定 |
+| 缺乏快取 | 每次重新分析 | 加入 Attribute 快取層 |
+| 語言支援 | 主要中文 | ✅ 已建立英文版 |
+
+---
+
+## 十、關鍵檔案清單
+
+### Backend
+- `app/routers/analyze.py` - Attribute Agent 路由
+- `app/routers/expert_transformation.py` - Expert Transformation 路由
+- `app/prompts/step_prompts.py` - Attribute Agent 提示詞
+- `app/prompts/expert_transformation_prompt.py` - Expert Transformation 提示詞
+- `app/services/expert_source_service.py` - 專家來源服務
+- `app/services/llm_service.py` - LLM 調用服務
+- `app/data/curated_occupations_zh.json` - 精選職業（中文）
+- `app/data/curated_occupations_en.json` - 精選職業（英文）
+- `app/data/dbpedia_occupations_en.json` - DBpedia 職業
+
+### Frontend
+- `src/App.tsx` - 主狀態管理
+- `src/hooks/useAttribute.ts` - Attribute Agent Hook
+- `src/hooks/useExpertTransformation.ts` - Expert Transformation Hook
+- `src/components/TransformationInputPanel.tsx` - 轉換控制面板
+- `src/types/index.ts` - 類型定義

backend/app/data/conceptnet_occupations_en.json

@@ -0,0 +1,9 @@
+{
+  "metadata": {
+    "source": "conceptnet",
+    "language": "en",
+    "fetched_at": "2025-12-04T07:26:30.695936+00:00",
+    "total_count": 0
+  },
+  "occupations": []
+}

backend/app/data/conceptnet_occupations_zh.json

@@ -0,0 +1,9 @@
+{
+  "metadata": {
+    "source": "conceptnet",
+    "language": "zh",
+    "fetched_at": "2025-12-04T07:26:26.994914+00:00",
+    "total_count": 0
+  },
+  "occupations": []
+}

backend/app/data/curated_occupations_en.json

@@ -0,0 +1,216 @@
+{
+  "metadata": {
+    "source": "curated",
+    "language": "en",
+    "created_at": "2025-12-04",
+    "total_count": 210,
+    "description": "Curated common professional occupations with specific domains"
+  },
+  "occupations": [
+    {"name": "Surgeon", "domain": "Healthcare"},
+    {"name": "Internist", "domain": "Healthcare"},
+    {"name": "Dentist", "domain": "Healthcare"},
+    {"name": "Ophthalmologist", "domain": "Healthcare"},
+    {"name": "Psychiatrist", "domain": "Healthcare"},
+    {"name": "Pediatrician", "domain": "Healthcare"},
+    {"name": "Nurse", "domain": "Healthcare"},
+    {"name": "Pharmacist", "domain": "Healthcare"},
+    {"name": "Clinical Psychologist", "domain": "Healthcare"},
+    {"name": "Physical Therapist", "domain": "Healthcare"},
+    {"name": "Occupational Therapist", "domain": "Healthcare"},
+    {"name": "Nutritionist", "domain": "Healthcare"},
+    {"name": "Traditional Chinese Medicine Doctor", "domain": "Healthcare"},
+    {"name": "Veterinarian", "domain": "Healthcare"},
+
+    {"name": "Software Engineer", "domain": "Information Technology"},
+    {"name": "Frontend Developer", "domain": "Information Technology"},
+    {"name": "Backend Developer", "domain": "Information Technology"},
+    {"name": "Data Scientist", "domain": "Information Technology"},
+    {"name": "Data Engineer", "domain": "Information Technology"},
+    {"name": "Machine Learning Engineer", "domain": "Information Technology"},
+    {"name": "Cybersecurity Engineer", "domain": "Information Technology"},
+    {"name": "DevOps Engineer", "domain": "Information Technology"},
+    {"name": "UI Designer", "domain": "Information Technology"},
+    {"name": "UX Designer", "domain": "Information Technology"},
+    {"name": "Product Manager", "domain": "Information Technology"},
+    {"name": "Systems Analyst", "domain": "Information Technology"},
+    {"name": "Network Engineer", "domain": "Information Technology"},
+    {"name": "Cloud Architect", "domain": "Information Technology"},
+
+    {"name": "Accountant", "domain": "Finance & Business"},
+    {"name": "Financial Analyst", "domain": "Finance & Business"},
+    {"name": "Investment Manager", "domain": "Finance & Business"},
+    {"name": "Risk Manager", "domain": "Finance & Business"},
+    {"name": "Actuary", "domain": "Finance & Business"},
+    {"name": "Bank Manager", "domain": "Finance & Business"},
+    {"name": "Securities Analyst", "domain": "Finance & Business"},
+    {"name": "Tax Consultant", "domain": "Finance & Business"},
+    {"name": "Business Consultant", "domain": "Finance & Business"},
+    {"name": "HR Manager", "domain": "Finance & Business"},
+    {"name": "Marketing Manager", "domain": "Finance & Business"},
+    {"name": "Sales Manager", "domain": "Finance & Business"},
+    {"name": "Procurement Manager", "domain": "Finance & Business"},
+    {"name": "Entrepreneur", "domain": "Finance & Business"},
+
+    {"name": "Lawyer", "domain": "Law & Policy"},
+    {"name": "Judge", "domain": "Law & Policy"},
+    {"name": "Prosecutor", "domain": "Law & Policy"},
+    {"name": "Notary", "domain": "Law & Policy"},
+    {"name": "Legal Counsel", "domain": "Law & Policy"},
+    {"name": "IP Attorney", "domain": "Law & Policy"},
+    {"name": "Policy Analyst", "domain": "Law & Policy"},
+    {"name": "Diplomat", "domain": "Law & Policy"},
+    {"name": "Civil Servant", "domain": "Law & Policy"},
+    {"name": "Legislator", "domain": "Law & Policy"},
+    {"name": "Mediator", "domain": "Law & Policy"},
+    {"name": "Legal Scholar", "domain": "Law & Policy"},
+
+    {"name": "University Professor", "domain": "Education & Academia"},
+    {"name": "High School Teacher", "domain": "Education & Academia"},
+    {"name": "Middle School Teacher", "domain": "Education & Academia"},
+    {"name": "Elementary School Teacher", "domain": "Education & Academia"},
+    {"name": "Preschool Teacher", "domain": "Education & Academia"},
+    {"name": "Special Education Teacher", "domain": "Education & Academia"},
+    {"name": "Tutor", "domain": "Education & Academia"},
+    {"name": "Researcher", "domain": "Education & Academia"},
+    {"name": "Librarian", "domain": "Education & Academia"},
+    {"name": "Education Administrator", "domain": "Education & Academia"},
+    {"name": "Academic Editor", "domain": "Education & Academia"},
+    {"name": "Education Consultant", "domain": "Education & Academia"},
+    {"name": "Speech Therapist", "domain": "Education & Academia"},
+
+    {"name": "Painter", "domain": "Arts & Creativity"},
+    {"name": "Sculptor", "domain": "Arts & Creativity"},
+    {"name": "Musician", "domain": "Arts & Creativity"},
+    {"name": "Composer", "domain": "Arts & Creativity"},
+    {"name": "Conductor", "domain": "Arts & Creativity"},
+    {"name": "Dancer", "domain": "Arts & Creativity"},
+    {"name": "Actor", "domain": "Arts & Creativity"},
+    {"name": "Film Director", "domain": "Arts & Creativity"},
+    {"name": "Screenwriter", "domain": "Arts & Creativity"},
+    {"name": "Photographer", "domain": "Arts & Creativity"},
+    {"name": "Illustrator", "domain": "Arts & Creativity"},
+    {"name": "Animator", "domain": "Arts & Creativity"},
+    {"name": "Graphic Designer", "domain": "Arts & Creativity"},
+    {"name": "Fashion Designer", "domain": "Arts & Creativity"},
+    {"name": "Jewelry Designer", "domain": "Arts & Creativity"},
+
+    {"name": "Mechanical Engineer", "domain": "Engineering & Manufacturing"},
+    {"name": "Electrical Engineer", "domain": "Engineering & Manufacturing"},
+    {"name": "Electronics Engineer", "domain": "Engineering & Manufacturing"},
+    {"name": "Chemical Engineer", "domain": "Engineering & Manufacturing"},
+    {"name": "Materials Engineer", "domain": "Engineering & Manufacturing"},
+    {"name": "Industrial Engineer", "domain": "Engineering & Manufacturing"},
+    {"name": "Automation Engineer", "domain": "Engineering & Manufacturing"},
+    {"name": "Quality Control Engineer", "domain": "Engineering & Manufacturing"},
+    {"name": "Process Engineer", "domain": "Engineering & Manufacturing"},
+    {"name": "R&D Engineer", "domain": "Engineering & Manufacturing"},
+    {"name": "Production Manager", "domain": "Engineering & Manufacturing"},
+    {"name": "Factory Manager", "domain": "Engineering & Manufacturing"},
+    {"name": "Technician", "domain": "Engineering & Manufacturing"},
+
+    {"name": "Architect", "domain": "Architecture & Space"},
+    {"name": "Interior Designer", "domain": "Architecture & Space"},
+    {"name": "Landscape Designer", "domain": "Architecture & Space"},
+    {"name": "Urban Planner", "domain": "Architecture & Space"},
+    {"name": "Structural Engineer", "domain": "Architecture & Space"},
+    {"name": "Civil Engineer", "domain": "Architecture & Space"},
+    {"name": "Construction Engineer", "domain": "Architecture & Space"},
+    {"name": "Site Supervisor", "domain": "Architecture & Space"},
+    {"name": "Surveyor", "domain": "Architecture & Space"},
+    {"name": "Architectural Drafter", "domain": "Architecture & Space"},
+    {"name": "Exhibition Designer", "domain": "Architecture & Space"},
+    {"name": "Lighting Designer", "domain": "Architecture & Space"},
+
+    {"name": "Journalist", "domain": "Media & Communications"},
+    {"name": "News Anchor", "domain": "Media & Communications"},
+    {"name": "Editor", "domain": "Media & Communications"},
+    {"name": "Copy Editor", "domain": "Media & Communications"},
+    {"name": "Video Editor", "domain": "Media & Communications"},
+    {"name": "PR Specialist", "domain": "Media & Communications"},
+    {"name": "Advertising Planner", "domain": "Media & Communications"},
+    {"name": "Social Media Manager", "domain": "Media & Communications"},
+    {"name": "Content Creator", "domain": "Media & Communications"},
+    {"name": "Podcast Host", "domain": "Media & Communications"},
+    {"name": "Publisher", "domain": "Media & Communications"},
+    {"name": "Translator", "domain": "Media & Communications"},
+    {"name": "Interpreter", "domain": "Media & Communications"},
+
+    {"name": "Agronomist", "domain": "Agriculture & Environment"},
+    {"name": "Horticulturist", "domain": "Agriculture & Environment"},
+    {"name": "Livestock Specialist", "domain": "Agriculture & Environment"},
+    {"name": "Aquaculture Specialist", "domain": "Agriculture & Environment"},
+    {"name": "Environmental Engineer", "domain": "Agriculture & Environment"},
+    {"name": "Ecologist", "domain": "Agriculture & Environment"},
+    {"name": "Forest Ranger", "domain": "Agriculture & Environment"},
+    {"name": "Meteorologist", "domain": "Agriculture & Environment"},
+    {"name": "Geologist", "domain": "Agriculture & Environment"},
+    {"name": "Environmental Inspector", "domain": "Agriculture & Environment"},
+    {"name": "Sustainability Consultant", "domain": "Agriculture & Environment"},
+    {"name": "Organic Farmer", "domain": "Agriculture & Environment"},
+
+    {"name": "Executive Chef", "domain": "Hospitality & Service"},
+    {"name": "Pastry Chef", "domain": "Hospitality & Service"},
+    {"name": "Bartender", "domain": "Hospitality & Service"},
+    {"name": "Sommelier", "domain": "Hospitality & Service"},
+    {"name": "Restaurant Manager", "domain": "Hospitality & Service"},
+    {"name": "Hotel Manager", "domain": "Hospitality & Service"},
+    {"name": "Travel Planner", "domain": "Hospitality & Service"},
+    {"name": "Tour Guide", "domain": "Hospitality & Service"},
+    {"name": "Barista", "domain": "Hospitality & Service"},
+    {"name": "Food Critic", "domain": "Hospitality & Service"},
+    {"name": "Wedding Planner", "domain": "Hospitality & Service"},
+    {"name": "Event Planner", "domain": "Hospitality & Service"},
+
+    {"name": "Sports Coach", "domain": "Sports & Fitness"},
+    {"name": "Personal Trainer", "domain": "Sports & Fitness"},
+    {"name": "Yoga Instructor", "domain": "Sports & Fitness"},
+    {"name": "Athletic Trainer", "domain": "Sports & Fitness"},
+    {"name": "Physical Education Teacher", "domain": "Sports & Fitness"},
+    {"name": "Sports Psychologist", "domain": "Sports & Fitness"},
+    {"name": "Sports Nutritionist", "domain": "Sports & Fitness"},
+    {"name": "Professional Athlete", "domain": "Sports & Fitness"},
+    {"name": "Referee", "domain": "Sports & Fitness"},
+    {"name": "Strength Coach", "domain": "Sports & Fitness"},
+    {"name": "Sports Agent", "domain": "Sports & Fitness"},
+
+    {"name": "Social Worker", "domain": "Social Services"},
+    {"name": "Counselor", "domain": "Social Services"},
+    {"name": "Guidance Counselor", "domain": "Social Services"},
+    {"name": "Volunteer Coordinator", "domain": "Social Services"},
+    {"name": "Nonprofit Manager", "domain": "Social Services"},
+    {"name": "Community Organizer", "domain": "Social Services"},
+    {"name": "Elderly Care Worker", "domain": "Social Services"},
+    {"name": "Youth Counselor", "domain": "Social Services"},
+    {"name": "Family Therapist", "domain": "Social Services"},
+    {"name": "Career Counselor", "domain": "Social Services"},
+    {"name": "Addiction Counselor", "domain": "Social Services"},
+
+    {"name": "Pilot", "domain": "Transportation & Logistics"},
+    {"name": "Ship Captain", "domain": "Transportation & Logistics"},
+    {"name": "Train Operator", "domain": "Transportation & Logistics"},
+    {"name": "Air Traffic Controller", "domain": "Transportation & Logistics"},
+    {"name": "Logistics Manager", "domain": "Transportation & Logistics"},
+    {"name": "Supply Chain Manager", "domain": "Transportation & Logistics"},
+    {"name": "Warehouse Manager", "domain": "Transportation & Logistics"},
+    {"name": "Customs Broker", "domain": "Transportation & Logistics"},
+    {"name": "Traffic Engineer", "domain": "Transportation & Logistics"},
+    {"name": "Port Authority Officer", "domain": "Transportation & Logistics"},
+
+    {"name": "Physicist", "domain": "Scientific Research"},
+    {"name": "Chemist", "domain": "Scientific Research"},
+    {"name": "Biologist", "domain": "Scientific Research"},
+    {"name": "Astronomer", "domain": "Scientific Research"},
+    {"name": "Mathematician", "domain": "Scientific Research"},
+    {"name": "Statistician", "domain": "Scientific Research"},
+    {"name": "Geneticist", "domain": "Scientific Research"},
+    {"name": "Neuroscientist", "domain": "Scientific Research"},
+    {"name": "Oceanographer", "domain": "Scientific Research"},
+    {"name": "Archaeologist", "domain": "Scientific Research"},
+    {"name": "Anthropologist", "domain": "Scientific Research"},
+    {"name": "Sociologist", "domain": "Scientific Research"},
+    {"name": "Economist", "domain": "Scientific Research"},
+    {"name": "Historian", "domain": "Scientific Research"},
+    {"name": "Philosopher", "domain": "Scientific Research"}
+  ]
+}

backend/app/data/curated_occupations_zh.json

@@ -0,0 +1,216 @@
+{
+  "metadata": {
+    "source": "curated",
+    "language": "zh",
+    "created_at": "2025-12-04",
+    "total_count": 210,
+    "description": "精選常見專家職業，含具體專業領域"
+  },
+  "occupations": [
+    {"name": "外科醫師", "domain": "醫療與健康"},
+    {"name": "內科醫師", "domain": "醫療與健康"},
+    {"name": "牙醫師", "domain": "醫療與健康"},
+    {"name": "眼科醫師", "domain": "醫療與健康"},
+    {"name": "精神科醫師", "domain": "醫療與健康"},
+    {"name": "小兒科醫師", "domain": "醫療與健康"},
+    {"name": "護理師", "domain": "醫療與健康"},
+    {"name": "藥師", "domain": "醫療與健康"},
+    {"name": "臨床心理師", "domain": "醫療與健康"},
+    {"name": "物理治療師", "domain": "醫療與健康"},
+    {"name": "職能治療師", "domain": "醫療與健康"},
+    {"name": "營養師", "domain": "醫療與健康"},
+    {"name": "中醫師", "domain": "醫療與健康"},
+    {"name": "獸醫師", "domain": "醫療與健康"},
+
+    {"name": "軟體工程師", "domain": "資訊科技"},
+    {"name": "前端工程師", "domain": "資訊科技"},
+    {"name": "後端工程師", "domain": "資訊科技"},
+    {"name": "資料科學家", "domain": "資訊科技"},
+    {"name": "資料工程師", "domain": "資訊科技"},
+    {"name": "機器學習工程師", "domain": "資訊科技"},
+    {"name": "資安工程師", "domain": "資訊科技"},
+    {"name": "DevOps工程師", "domain": "資訊科技"},
+    {"name": "UI設計師", "domain": "資訊科技"},
+    {"name": "UX設計師", "domain": "資訊科技"},
+    {"name": "產品經理", "domain": "資訊科技"},
+    {"name": "系統分析師", "domain": "資訊科技"},
+    {"name": "網路工程師", "domain": "資訊科技"},
+    {"name": "雲端架構師", "domain": "資訊科技"},
+
+    {"name": "會計師", "domain": "金融與商業"},
+    {"name": "財務分析師", "domain": "金融與商業"},
+    {"name": "投資經理", "domain": "金融與商業"},
+    {"name": "風險管理師", "domain": "金融與商業"},
+    {"name": "精算師", "domain": "金融與商業"},
+    {"name": "銀行經理", "domain": "金融與商業"},
+    {"name": "證券分析師", "domain": "金融與商業"},
+    {"name": "稅務顧問", "domain": "金融與商業"},
+    {"name": "企業顧問", "domain": "金融與商業"},
+    {"name": "人資經理", "domain": "金融與商業"},
+    {"name": "行銷經理", "domain": "金融與商業"},
+    {"name": "業務經理", "domain": "金融與商業"},
+    {"name": "採購經理", "domain": "金融與商業"},
+    {"name": "創業家", "domain": "金融與商業"},
+
+    {"name": "律師", "domain": "法律與政策"},
+    {"name": "法官", "domain": "法律與政策"},
+    {"name": "檢察官", "domain": "法律與政策"},
+    {"name": "公證人", "domain": "法律與政策"},
+    {"name": "法務專員", "domain": "法律與政策"},
+    {"name": "智財律師", "domain": "法律與政策"},
+    {"name": "政策分析師", "domain": "法律與政策"},
+    {"name": "外交官", "domain": "法律與政策"},
+    {"name": "公務員", "domain": "法律與政策"},
+    {"name": "立法委員", "domain": "法律與政策"},
+    {"name": "調解員", "domain": "法律與政策"},
+    {"name": "法律學者", "domain": "法律與政策"},
+
+    {"name": "大學教授", "domain": "教育與學術"},
+    {"name": "高中教師", "domain": "教育與學術"},
+    {"name": "國中教師", "domain": "教育與學術"},
+    {"name": "小學教師", "domain": "教育與學術"},
+    {"name": "幼教老師", "domain": "教育與學術"},
+    {"name": "特教老師", "domain": "教育與學術"},
+    {"name": "補習班老師", "domain": "教育與學術"},
+    {"name": "研究員", "domain": "教育與學術"},
+    {"name": "圖書館員", "domain": "教育與學術"},
+    {"name": "教育行政人員", "domain": "教育與學術"},
+    {"name": "學術編輯", "domain": "教育與學術"},
+    {"name": "教育顧問", "domain": "教育與學術"},
+    {"name": "語言治療師", "domain": "教育與學術"},
+
+    {"name": "畫家", "domain": "藝術與創意"},
+    {"name": "雕塑家", "domain": "藝術與創意"},
+    {"name": "音樂家", "domain": "藝術與創意"},
+    {"name": "作曲家", "domain": "藝術與創意"},
+    {"name": "指揮家", "domain": "藝術與創意"},
+    {"name": "舞蹈家", "domain": "藝術與創意"},
+    {"name": "演員", "domain": "藝術與創意"},
+    {"name": "導演", "domain": "藝術與創意"},
+    {"name": "編劇", "domain": "藝術與創意"},
+    {"name": "攝影師", "domain": "藝術與創意"},
+    {"name": "插畫家", "domain": "藝術與創意"},
+    {"name": "動畫師", "domain": "藝術與創意"},
+    {"name": "平面設計師", "domain": "藝術與創意"},
+    {"name": "時尚設計師", "domain": "藝術與創意"},
+    {"name": "珠寶設計師", "domain": "藝術與創意"},
+
+    {"name": "機械工程師", "domain": "工程與製造"},
+    {"name": "電機工程師", "domain": "工程與製造"},
+    {"name": "電子工程師", "domain": "工程與製造"},
+    {"name": "化學工程師", "domain": "工程與製造"},
+    {"name": "材料工程師", "domain": "工程與製造"},
+    {"name": "工業工程師", "domain": "工程與製造"},
+    {"name": "自動化工程師", "domain": "工程與製造"},
+    {"name": "品管工程師", "domain": "工程與製造"},
+    {"name": "製程工程師", "domain": "工程與製造"},
+    {"name": "研發工程師", "domain": "工程與製造"},
+    {"name": "生產經理", "domain": "工程與製造"},
+    {"name": "工廠廠長", "domain": "工程與製造"},
+    {"name": "技師", "domain": "工程與製造"},
+
+    {"name": "建築師", "domain": "建築與空間"},
+    {"name": "室內設計師", "domain": "建築與空間"},
+    {"name": "景觀設計師", "domain": "建築與空間"},
+    {"name": "都市規劃師", "domain": "建築與空間"},
+    {"name": "結構工程師", "domain": "建築與空間"},
+    {"name": "土木工程師", "domain": "建築與空間"},
+    {"name": "營造工程師", "domain": "建築與空間"},
+    {"name": "工地主任", "domain": "建築與空間"},
+    {"name": "測量師", "domain": "建築與空間"},
+    {"name": "建築繪圖員", "domain": "建築與空間"},
+    {"name": "展場設計師", "domain": "建築與空間"},
+    {"name": "燈光設計師", "domain": "建築與空間"},
+
+    {"name": "記者", "domain": "媒體與傳播"},
+    {"name": "主播", "domain": "媒體與傳播"},
+    {"name": "編輯", "domain": "媒體與傳播"},
+    {"name": "文字編輯", "domain": "媒體與傳播"},
+    {"name": "影片剪輯師", "domain": "媒體與傳播"},
+    {"name": "公關專員", "domain": "媒體與傳播"},
+    {"name": "廣告企劃", "domain": "媒體與傳播"},
+    {"name": "社群經理", "domain": "媒體與傳播"},
+    {"name": "內容創作者", "domain": "媒體與傳播"},
+    {"name": "播客主持人", "domain": "媒體與傳播"},
+    {"name": "出版人", "domain": "媒體與傳播"},
+    {"name": "翻譯師", "domain": "媒體與傳播"},
+    {"name": "口譯員", "domain": "媒體與傳播"},
+
+    {"name": "農藝師", "domain": "農業與環境"},
+    {"name": "園藝師", "domain": "農業與環境"},
+    {"name": "畜牧專家", "domain": "農業與環境"},
+    {"name": "水產養殖師", "domain": "農業與環境"},
+    {"name": "環境工程師", "domain": "農業與環境"},
+    {"name": "生態學家", "domain": "農業與環境"},
+    {"name": "森林保育員", "domain": "農業與環境"},
+    {"name": "氣象學家", "domain": "農業與環境"},
+    {"name": "地質學家", "domain": "農業與環境"},
+    {"name": "環保稽查員", "domain": "農業與環境"},
+    {"name": "永續發展顧問", "domain": "農業與環境"},
+    {"name": "有機農場主", "domain": "農業與環境"},
+
+    {"name": "主廚", "domain": "餐飲與服務"},
+    {"name": "西點師傅", "domain": "餐飲與服務"},
+    {"name": "調酒師", "domain": "餐飲與服務"},
+    {"name": "侍酒師", "domain": "餐飲與服務"},
+    {"name": "餐廳經理", "domain": "餐飲與服務"},
+    {"name": "飯店經理", "domain": "餐飲與服務"},
+    {"name": "旅遊規劃師", "domain": "餐飲與服務"},
+    {"name": "導遊", "domain": "餐飲與服務"},
+    {"name": "咖啡師", "domain": "餐飲與服務"},
+    {"name": "美食評論家", "domain": "餐飲與服務"},
+    {"name": "婚禮策劃師", "domain": "餐飲與服務"},
+    {"name": "活動企劃", "domain": "餐飲與服務"},
+
+    {"name": "運動教練", "domain": "運動與健身"},
+    {"name": "健身教練", "domain": "運動與健身"},
+    {"name": "瑜珈老師", "domain": "運動與健身"},
+    {"name": "運動防護員", "domain": "運動與健身"},
+    {"name": "體育老師", "domain": "運動與健身"},
+    {"name": "運動心理師", "domain": "運動與健身"},
+    {"name": "運動營養師", "domain": "運動與健身"},
+    {"name": "職業運動員", "domain": "運動與健身"},
+    {"name": "裁判", "domain": "運動與健身"},
+    {"name": "體能訓練師", "domain": "運動與健身"},
+    {"name": "運動經紀人", "domain": "運動與健身"},
+
+    {"name": "社工師", "domain": "社會服務"},
+    {"name": "心理諮商師", "domain": "社會服務"},
+    {"name": "輔導員", "domain": "社會服務"},
+    {"name": "志工協調員", "domain": "社會服務"},
+    {"name": "非營利組織經理", "domain": "社會服務"},
+    {"name": "社區營造員", "domain": "社會服務"},
+    {"name": "長照服務員", "domain": "社會服務"},
+    {"name": "青少年輔導員", "domain": "社會服務"},
+    {"name": "家庭治療師", "domain": "社會服務"},
+    {"name": "職涯諮詢師", "domain": "社會服務"},
+    {"name": "戒癮輔導員", "domain": "社會服務"},
+
+    {"name": "飛行員", "domain": "交通與物流"},
+    {"name": "船長", "domain": "交通與物流"},
+    {"name": "火車駕駛", "domain": "交通與物流"},
+    {"name": "航空管制員", "domain": "交通與物流"},
+    {"name": "物流經理", "domain": "交通與物流"},
+    {"name": "供應鏈經理", "domain": "交通與物流"},
+    {"name": "倉儲經理", "domain": "交通與物流"},
+    {"name": "報關員", "domain": "交通與物流"},
+    {"name": "交通工程師", "domain": "交通與物流"},
+    {"name": "港務人員", "domain": "交通與物流"},
+
+    {"name": "物理學家", "domain": "科學研究"},
+    {"name": "化學家", "domain": "科學研究"},
+    {"name": "生物學家", "domain": "科學研究"},
+    {"name": "天文學家", "domain": "科學研究"},
+    {"name": "數學家", "domain": "科學研究"},
+    {"name": "統計學家", "domain": "科學研究"},
+    {"name": "基因學家", "domain": "科學研究"},
+    {"name": "神經科學家", "domain": "科學研究"},
+    {"name": "海洋學家", "domain": "科學研究"},
+    {"name": "考古學家", "domain": "科學研究"},
+    {"name": "人類學家", "domain": "科學研究"},
+    {"name": "社會學家", "domain": "科學研究"},
+    {"name": "經濟學家", "domain": "科學研究"},
+    {"name": "歷史學家", "domain": "科學研究"},
+    {"name": "哲學家", "domain": "科學研究"}
+  ]
+}

backend/app/models/schemas.py

@@ -209,8 +209,9 @@ class ExpertTransformationDAGResult(BaseModel):
 class ExpertSource(str, Enum):
     """專家來源類型"""
     LLM = "llm"
+    CURATED = "curated"  # 精選職業（210筆，含具體領域）
+    DBPEDIA = "dbpedia"
     WIKIDATA = "wikidata"
-    CONCEPTNET = "conceptnet"
 
 
 class ExpertTransformationRequest(BaseModel):
@@ -226,7 +227,7 @@ class ExpertTransformationRequest(BaseModel):
 
     # Expert source parameters
     expert_source: ExpertSource = ExpertSource.LLM  # 專家來源
-    expert_language: str = "zh"                     # 外部來源的語言
+    expert_language: str = "en"                     # 外部來源的語言 (目前只有英文資料)
 
     # LLM parameters
     model: Optional[str] = None

backend/app/prompts/expert_transformation_prompt.py

@@ -53,19 +53,32 @@ def get_expert_keyword_generation_prompt(
     keywords_per_expert: int = 1
 ) -> str:
     """Step 1: 專家視角關鍵字生成"""
-    experts_info = ", ".join([f"{exp['id']}:{exp['name']}({exp['domain']})" for exp in experts])
+    # 建立專家列表，格式更清晰
+    experts_list = "\n".join([f"- {exp['id']}: {exp['name']}" for exp in experts])
 
     return f"""/no_think
-專家團隊：{experts_info}
+你需要扮演以下專家，為屬性生成創新關鍵字：
-屬性：「{attribute}」（{category}）
 
-每位專家從自己的專業視角為此屬性生成 {keywords_per_expert} 個創新關鍵字（2-6字）。
+【專家名單】
-關鍵字要反映該專家領域的獨特思考方式。
+{experts_list}
+
+【任務】
+屬性：「{attribute}」（類別：{category}）
+
+請為每位專家：
+1. 先理解該職業的專業背景、知識領域、工作內容
+2. 從該職業的獨特視角思考「{attribute}」
+3. 生成 {keywords_per_expert} 個與該專業相關的創新關鍵字（2-6字）
+
+關鍵字必須反映該專家的專業思維方式，例如：
+- 會計師 看「移動」→「資金流動」「成本效益」
+- 建築師 看「移動」→「動線設計」「空間流動」
+- 心理師 看「移動」→「行為動機」「情緒轉變」
 
 回傳 JSON：
 {{"keywords": [{{"keyword": "詞彙", "expert_id": "expert-X", "expert_name": "名稱"}}, ...]}}
 
-共需 {len(experts) * keywords_per_expert} 個關鍵字。"""
+共需 {len(experts) * keywords_per_expert} 個關鍵字，每個關鍵字必須明顯與對應專家的專業領域相關。"""
 
 
 def get_single_description_prompt(
@@ -76,12 +89,17 @@ def get_single_description_prompt(
     expert_domain: str
 ) -> str:
     """Step 2: 為單一關鍵字生成描述"""
+    # 如果 domain 是通用的，就只用職業名稱
+    domain_text = f"（{expert_domain}）" if expert_domain and expert_domain != "Professional Field" else ""
+
     return f"""/no_think
 物件：「{query}」
-專家：{expert_name}（{expert_domain}）
+專家：{expert_name}{domain_text}
 關鍵字：{keyword}
 
-從這位專家的視角，生成一段創新應用描述（15-30字），說明如何將「{keyword}」的概念應用到「{query}」上。
+你是一位{expert_name}。從你的專業視角，生成一段創新應用描述（15-30字），說明如何將「{keyword}」的概念應用到「{query}」上。
+
+描述要體現{expert_name}的專業思維和獨特觀點。
 
 回傳 JSON：
 {{"description": "應用描述"}}"""

backend/app/routers/expert_transformation.py

@@ -37,16 +37,29 @@ async def generate_expert_transformation_events(
         model = request.model
 
         # ========== Step 0: Generate expert team ==========
+        logger.info(f"[DEBUG] expert_source from request: {request.expert_source}")
+        logger.info(f"[DEBUG] expert_source value: {request.expert_source.value}")
+        logger.info(f"[DEBUG] custom_experts: {request.custom_experts}")
+
         yield f"event: expert_start\ndata: {json.dumps({'message': '正在組建專家團隊...', 'source': request.expert_source.value}, ensure_ascii=False)}\n\n"
 
         experts: List[ExpertProfile] = []
         actual_source = request.expert_source.value
 
+        # 過濾出實際有內容的自訂專家（排除空字串）
+        actual_custom_experts = [
+            e.strip() for e in (request.custom_experts or [])
+            if e and e.strip()
+        ]
+        logger.info(f"[DEBUG] actual_custom_experts (filtered): {actual_custom_experts}")
+
         # 決定使用哪種來源生成專家
+        # 只有在明確選擇 LLM 或有實際自訂專家時才使用 LLM
         use_llm = (
             request.expert_source == ExpertSource.LLM or
-            request.custom_experts  # 有自訂專家時，使用 LLM 補充
+            len(actual_custom_experts) > 0  # 有實際自訂專家時，使用 LLM 補充
         )
+        logger.info(f"[DEBUG] use_llm decision: {use_llm}")
 
         if use_llm:
             # LLM 生成專家
@@ -55,7 +68,7 @@ async def generate_expert_transformation_events(
                     query=request.query,
                     categories=all_categories,
                     expert_count=request.expert_count,
-                    custom_experts=request.custom_experts
+                    custom_experts=actual_custom_experts if actual_custom_experts else None
                 )
                 logger.info(f"Expert prompt: {expert_prompt[:200]}")
 
@@ -78,9 +91,9 @@ async def generate_expert_transformation_events(
                 yield f"event: error\ndata: {json.dumps({'error': f'專家團隊生成失敗: {str(e)}'}, ensure_ascii=False)}\n\n"
                 return
         else:
-            # 外部來源生成專家
+            # 外部來源生成專家 (本地檔案，同步)
             try:
-                experts_data, actual_source = await expert_source_service.get_experts(
+                experts_data, actual_source = expert_source_service.get_experts(
                     source=request.expert_source.value,
                     count=request.expert_count,
                     language=request.expert_language
@@ -106,7 +119,7 @@ async def generate_expert_transformation_events(
                         query=request.query,
                         categories=all_categories,
                         expert_count=request.expert_count,
-                        custom_experts=request.custom_experts
+                        custom_experts=actual_custom_experts if actual_custom_experts else None
                     )
 
                     expert_response = await ollama_provider.generate(

backend/app/services/expert_cache.py

@@ -1,92 +0,0 @@
-"""Expert 資料快取模組
-
-提供 TTL-based 快取機制，減少外部 API 呼叫。
-"""
-
-import time
-from dataclasses import dataclass
-from typing import Dict, List, Optional
-
-
-@dataclass
-class CacheEntry:
-    """快取項目"""
-    data: List[dict]
-    timestamp: float
-
-
-class ExpertCache:
-    """TTL 快取，用於儲存外部來源的職業資料"""
-
-    def __init__(self, ttl_seconds: int = 3600):
-        """
-        初始化快取
-
-        Args:
-            ttl_seconds: 快取存活時間（預設 1 小時）
-        """
-        self._cache: Dict[str, CacheEntry] = {}
-        self._ttl = ttl_seconds
-
-    def get(self, key: str) -> Optional[List[dict]]:
-        """
-        取得快取資料
-
-        Args:
-            key: 快取鍵（如 "wikidata:zh:occupations"）
-
-        Returns:
-            快取的資料列表，若不存在或已過期則回傳 None
-        """
-        entry = self._cache.get(key)
-        if entry is None:
-            return None
-
-        # 檢查是否過期
-        if time.time() - entry.timestamp > self._ttl:
-            del self._cache[key]
-            return None
-
-        return entry.data
-
-    def set(self, key: str, data: List[dict]) -> None:
-        """
-        設定快取資料
-
-        Args:
-            key: 快取鍵
-            data: 要快取的資料列表
-        """
-        self._cache[key] = CacheEntry(
-            data=data,
-            timestamp=time.time()
-        )
-
-    def invalidate(self, key: Optional[str] = None) -> None:
-        """
-        清除快取
-
-        Args:
-            key: 要清除的鍵，若為 None 則清除全部
-        """
-        if key is None:
-            self._cache.clear()
-        elif key in self._cache:
-            del self._cache[key]
-
-    def get_stats(self) -> dict:
-        """取得快取統計資訊"""
-        now = time.time()
-        valid_count = sum(
-            1 for entry in self._cache.values()
-            if now - entry.timestamp <= self._ttl
-        )
-        return {
-            "total_entries": len(self._cache),
-            "valid_entries": valid_count,
-            "ttl_seconds": self._ttl
-        }
-
-
-# 全域快取實例
-expert_cache = ExpertCache()

backend/app/services/expert_source_service.py

@@ -1,293 +1,111 @@
-"""Expert 外部資料來源服務
+"""Expert 本地資料來源服務
 
-提供從 Wikidata SPARQL 和 ConceptNet API 獲取職業/領域資料的功能。
+從本地 JSON 檔案讀取職業資料，提供隨機選取功能。
 """
 
+import json
 import logging
 import random
-from abc import ABC, abstractmethod
+from pathlib import Path
-from typing import List, Optional, Tuple
+from typing import List, Tuple
-
-import httpx
-
-from .expert_cache import expert_cache
 
 logger = logging.getLogger(__name__)
 
+# 資料目錄
+DATA_DIR = Path(__file__).parent.parent / "data"
 
-class ExpertSourceProvider(ABC):
-    """外部來源提供者抽象類"""
 
-    @abstractmethod
+class LocalDataProvider:
-    async def fetch_occupations(
+    """從本地 JSON 檔案讀取職業資料"""
-        self, count: int, language: str = "zh"
+
-    ) -> List[dict]:
+    def __init__(self, source: str):
         """
-        獲取職業列表
+        Args:
+            source: 資料來源名稱 (dbpedia/wikidata)
+        """
+        self.source = source
+        self._cache: dict = {}  # 記憶體快取
+
+    def load_occupations(self, language: str = "en") -> List[dict]:
+        """
+        載入職業資料
 
         Args:
-            count: 需要的職業數量
+            language: 語言代碼 (en/zh)
-            language: 語言代碼 (zh/en)
 
         Returns:
-            職業資料列表 [{"name": "...", "domain": "..."}, ...]
+            職業列表 [{"name": "...", "domain": "..."}, ...]
         """
-        pass
+        cache_key = f"{self.source}:{language}"
 
+        # 檢查記憶體快取
+        if cache_key in self._cache:
+            return self._cache[cache_key]
 
-class WikidataProvider(ExpertSourceProvider):
+        # 讀取檔案
-    """Wikidata SPARQL 查詢提供者"""
+        file_path = DATA_DIR / f"{self.source}_occupations_{language}.json"
 
-    ENDPOINT = "https://query.wikidata.org/sparql"
+        if not file_path.exists():
-
+            logger.warning(f"資料檔案不存在: {file_path}")
-    def __init__(self):
+            return []
-        self.client = httpx.AsyncClient(timeout=30.0)
-
-    async def fetch_occupations(
-        self, count: int, language: str = "zh"
-    ) -> List[dict]:
-        """從 Wikidata 獲取職業列表"""
-        cache_key = f"wikidata:{language}:occupations"
-
-        # 檢查快取
-        cached = expert_cache.get(cache_key)
-        if cached:
-            logger.info(f"Wikidata cache hit: {len(cached)} occupations")
-            return self._random_select(cached, count)
-
-        # SPARQL 查詢
-        query = self._build_sparql_query(language)
 
         try:
-            response = await self.client.get(
+            with open(file_path, "r", encoding="utf-8") as f:
-                self.ENDPOINT,
+                data = json.load(f)
-                params={"query": query, "format": "json"},
-                headers={"Accept": "application/sparql-results+json"}
-            )
-            response.raise_for_status()
 
-            data = response.json()
+            occupations = data.get("occupations", [])
-            occupations = self._parse_sparql_response(data, language)
+            logger.info(f"載入 {len(occupations)} 筆 {self.source} {language} 職業")
 
-            if occupations:
+            # 存入快取
-                expert_cache.set(cache_key, occupations)
+            self._cache[cache_key] = occupations
-                logger.info(f"Wikidata fetched: {len(occupations)} occupations")
+            return occupations
-
-            return self._random_select(occupations, count)
 
         except Exception as e:
-            logger.error(f"Wikidata query failed: {e}")
+            logger.error(f"讀取職業資料失敗: {e}")
-            raise
+            return []
 
-    def _build_sparql_query(self, language: str) -> str:
+    def random_select(self, count: int, language: str = "en") -> List[dict]:
-        """建構 SPARQL 查詢"""
-        lang_filter = f'FILTER(LANG(?occupationLabel) = "{language}")'
-
-        return f"""
-        SELECT DISTINCT ?occupation ?occupationLabel ?fieldLabel WHERE {{
-          ?occupation wdt:P31 wd:Q28640.
-          ?occupation rdfs:label ?occupationLabel.
-          {lang_filter}
-
-          OPTIONAL {{
-            ?occupation wdt:P425 ?field.
-            ?field rdfs:label ?fieldLabel.
-            FILTER(LANG(?fieldLabel) = "{language}")
-          }}
-        }}
-        LIMIT 500
         """
+        隨機選取指定數量的職業
 
-    def _parse_sparql_response(self, data: dict, language: str) -> List[dict]:
+        Args:
-        """解析 SPARQL 回應"""
+            count: 需要的數量
-        results = []
+            language: 語言代碼
-        bindings = data.get("results", {}).get("bindings", [])
 
-        for item in bindings:
+        Returns:
-            name = item.get("occupationLabel", {}).get("value", "")
+            隨機選取的職業列表
-            field = item.get("fieldLabel", {}).get("value", "")
+        """
+        all_occupations = self.load_occupations(language)
 
-            if name and len(name) >= 2:
+        if not all_occupations:
-                results.append({
+            return []
-                    "name": name,
-                    "domain": field if field else self._infer_domain(name)
-                })
 
-        return results
+        if len(all_occupations) <= count:
+            return all_occupations
 
-    def _infer_domain(self, occupation_name: str) -> str:
+        return random.sample(all_occupations, count)
-        """根據職業名稱推斷領域"""
-        # 簡單的領域推斷規則
-        domain_keywords = {
-            "醫": "醫療健康",
-            "師": "專業服務",
-            "工程": "工程技術",
-            "設計": "設計創意",
-            "藝術": "藝術文化",
-            "運動": "體育運動",
-            "農": "農業",
-            "漁": "漁業",
-            "商": "商業貿易",
-            "法": "法律",
-            "教": "教育",
-            "研究": "學術研究",
-        }
-
-        for keyword, domain in domain_keywords.items():
-            if keyword in occupation_name:
-                return domain
-
-        return "專業領域"
-
-    def _random_select(self, items: List[dict], count: int) -> List[dict]:
-        """隨機選取指定數量"""
-        if len(items) <= count:
-            return items
-        return random.sample(items, count)
-
-    async def close(self):
-        await self.client.aclose()
-
-
-class ConceptNetProvider(ExpertSourceProvider):
-    """ConceptNet API 查詢提供者"""
-
-    ENDPOINT = "https://api.conceptnet.io"
-
-    def __init__(self):
-        self.client = httpx.AsyncClient(timeout=30.0)
-
-    async def fetch_occupations(
-        self, count: int, language: str = "zh"
-    ) -> List[dict]:
-        """從 ConceptNet 獲取職業相關概念"""
-        cache_key = f"conceptnet:{language}:occupations"
-
-        # 檢查快取
-        cached = expert_cache.get(cache_key)
-        if cached:
-            logger.info(f"ConceptNet cache hit: {len(cached)} concepts")
-            return self._random_select(cached, count)
-
-        # 查詢職業相關概念
-        lang_code = "zh" if language == "zh" else "en"
-        start_concept = f"/c/{lang_code}/職業" if lang_code == "zh" else f"/c/{lang_code}/occupation"
-
-        try:
-            occupations = []
-
-            # 查詢 IsA 關係
-            response = await self.client.get(
-                f"{self.ENDPOINT}/query",
-                params={
-                    "start": start_concept,
-                    "rel": "/r/IsA",
-                    "limit": 100
-                }
-            )
-            response.raise_for_status()
-            data = response.json()
-            occupations.extend(self._parse_conceptnet_response(data, lang_code))
-
-            # 也查詢 RelatedTo 關係以獲取更多結果
-            response2 = await self.client.get(
-                f"{self.ENDPOINT}/query",
-                params={
-                    "node": start_concept,
-                    "rel": "/r/RelatedTo",
-                    "limit": 100
-                }
-            )
-            response2.raise_for_status()
-            data2 = response2.json()
-            occupations.extend(self._parse_conceptnet_response(data2, lang_code))
-
-            # 去重
-            seen = set()
-            unique_occupations = []
-            for occ in occupations:
-                if occ["name"] not in seen:
-                    seen.add(occ["name"])
-                    unique_occupations.append(occ)
-
-            if unique_occupations:
-                expert_cache.set(cache_key, unique_occupations)
-                logger.info(f"ConceptNet fetched: {len(unique_occupations)} concepts")
-
-            return self._random_select(unique_occupations, count)
-
-        except Exception as e:
-            logger.error(f"ConceptNet query failed: {e}")
-            raise
-
-    def _parse_conceptnet_response(self, data: dict, lang_code: str) -> List[dict]:
-        """解析 ConceptNet 回應"""
-        results = []
-        edges = data.get("edges", [])
-
-        for edge in edges:
-            # 取得 start 或 end 節點（取決於查詢方向）
-            start = edge.get("start", {})
-            end = edge.get("end", {})
-
-            # 選擇非起始節點的概念
-            node = end if start.get("@id", "").endswith("職業") or start.get("@id", "").endswith("occupation") else start
-
-            label = node.get("label", "")
-            term = node.get("term", "")
-
-            # 過濾：確保是目標語言且有意義
-            node_id = node.get("@id", "")
-            if f"/c/{lang_code}/" in node_id and label and len(label) >= 2:
-                results.append({
-                    "name": label,
-                    "domain": self._infer_domain_from_edge(edge)
-                })
-
-        return results
-
-    def _infer_domain_from_edge(self, edge: dict) -> str:
-        """從 edge 資訊推斷領域"""
-        # ConceptNet 的 edge 包含 surfaceText 可能有額外資訊
-        surface = edge.get("surfaceText", "")
-        rel = edge.get("rel", {}).get("label", "")
-
-        if "專業" in surface:
-            return "專業領域"
-        elif "技術" in surface:
-            return "技術領域"
-        else:
-            return "知識領域"
-
-    def _random_select(self, items: List[dict], count: int) -> List[dict]:
-        """隨機選取指定數量"""
-        if len(items) <= count:
-            return items
-        return random.sample(items, count)
-
-    async def close(self):
-        await self.client.aclose()
 
 
 class ExpertSourceService:
     """統一的專家來源服務"""
 
     def __init__(self):
-        self.wikidata = WikidataProvider()
+        self.curated = LocalDataProvider("curated")  # 精選職業
-        self.conceptnet = ConceptNetProvider()
+        self.dbpedia = LocalDataProvider("dbpedia")
+        self.wikidata = LocalDataProvider("wikidata")
 
-    async def get_experts(
+    def get_experts(
         self,
         source: str,
         count: int,
-        language: str = "zh",
+        language: str = "en",
         fallback_to_llm: bool = True
     ) -> Tuple[List[dict], str]:
         """
         從指定來源獲取專家資料
 
         Args:
-            source: 來源類型 ("wikidata" | "conceptnet")
+            source: 來源類型 ("dbpedia" | "wikidata")
             count: 需要的專家數量
             language: 語言代碼
             fallback_to_llm: 失敗時是否允許 fallback（由呼叫者處理）
@@ -296,35 +114,87 @@ class ExpertSourceService:
             (專家資料列表, 實際使用的來源)
 
         Raises:
-            Exception: 當獲取失敗且不 fallback 時
+            ValueError: 當獲取失敗且資料為空時
         """
-        provider = self._get_provider(source)
+        # 選擇 provider
-
+        if source == "curated":
-        try:
+            provider = self.curated
-            experts = await provider.fetch_occupations(count, language)
+            # 精選職業支援 zh 和 en，預設使用 zh
-
+            if language not in ["zh", "en"]:
-            if not experts:
+                language = "zh"
-                raise ValueError(f"No occupations found from {source}")
+        elif source == "wikidata":
-
+            provider = self.wikidata
-            return experts, source
-
-        except Exception as e:
-            logger.warning(f"Failed to fetch from {source}: {e}")
-            raise
-
-    def _get_provider(self, source: str) -> ExpertSourceProvider:
-        """根據來源類型取得對應的 provider"""
-        if source == "wikidata":
-            return self.wikidata
-        elif source == "conceptnet":
-            return self.conceptnet
         else:
-            raise ValueError(f"Unknown source: {source}")
+            # 預設使用 dbpedia
+            provider = self.dbpedia
+            source = "dbpedia"
 
-    async def close(self):
+        experts = provider.random_select(count, language)
-        """關閉所有 HTTP clients"""
+
-        await self.wikidata.close()
+        if not experts:
-        await self.conceptnet.close()
+            raise ValueError(f"No occupations found from {source} ({language})")
+
+        logger.info(f"從 {source} 取得 {len(experts)} 位專家")
+        return experts, source
+
+    def get_available_sources(self) -> List[dict]:
+        """
+        取得可用的資料來源資訊
+
+        Returns:
+            來源資訊列表
+        """
+        sources = []
+
+        # 檢查精選職業（中文）
+        curated_zh = DATA_DIR / "curated_occupations_zh.json"
+        if curated_zh.exists():
+            with open(curated_zh, "r", encoding="utf-8") as f:
+                data = json.load(f)
+                sources.append({
+                    "source": "curated",
+                    "language": "zh",
+                    "count": data["metadata"]["total_count"],
+                    "created_at": data["metadata"]["created_at"]
+                })
+
+        # 檢查精選職業（英文）
+        curated_en = DATA_DIR / "curated_occupations_en.json"
+        if curated_en.exists():
+            with open(curated_en, "r", encoding="utf-8") as f:
+                data = json.load(f)
+                sources.append({
+                    "source": "curated",
+                    "language": "en",
+                    "count": data["metadata"]["total_count"],
+                    "created_at": data["metadata"]["created_at"]
+                })
+
+        # 檢查 DBpedia
+        dbpedia_en = DATA_DIR / "dbpedia_occupations_en.json"
+        if dbpedia_en.exists():
+            with open(dbpedia_en, "r", encoding="utf-8") as f:
+                data = json.load(f)
+                sources.append({
+                    "source": "dbpedia",
+                    "language": "en",
+                    "count": data["metadata"]["total_count"],
+                    "fetched_at": data["metadata"]["fetched_at"]
+                })
+
+        # 檢查 Wikidata
+        wikidata_zh = DATA_DIR / "wikidata_occupations_zh.json"
+        if wikidata_zh.exists():
+            with open(wikidata_zh, "r", encoding="utf-8") as f:
+                data = json.load(f)
+                sources.append({
+                    "source": "wikidata",
+                    "language": "zh",
+                    "count": data["metadata"]["total_count"],
+                    "fetched_at": data["metadata"]["fetched_at"]
+                })
+
+        return sources
 
 
 # 全域服務實例

backend/scripts/fetch_occupations.py

@@ -0,0 +1,386 @@
+#!/usr/bin/env python3
+"""
+職業資料抓取腳本
+
+從 Wikidata SPARQL 和 ConceptNet API 抓取職業資料，
+儲存為本地 JSON 檔案供應用程式使用。
+
+使用方式:
+    cd backend
+    python scripts/fetch_occupations.py
+"""
+
+import json
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import List
+
+import httpx
+
+# 輸出目錄
+DATA_DIR = Path(__file__).parent.parent / "app" / "data"
+
+
+def fetch_wikidata_occupations(language: str) -> List[dict]:
+    """
+    從 Wikidata SPARQL 端點抓取所有職業（使用分頁）
+
+    Args:
+        language: 語言代碼 (zh/en)
+
+    Returns:
+        職業列表 [{"name": "...", "domain": "..."}, ...]
+    """
+    print(f"[Wikidata] 正在抓取 {language} 職業資料（分頁模式）...")
+
+    endpoint = "https://query.wikidata.org/sparql"
+    page_size = 500  # 每頁筆數
+    all_bindings = []
+    offset = 0
+
+    try:
+        with httpx.Client(timeout=120.0) as client:
+            while True:
+                # SPARQL 查詢 - 使用 SERVICE wikibase:label (更高效)
+                query = f"""
+                SELECT DISTINCT ?occupation ?occupationLabel ?fieldLabel WHERE {{
+                  ?occupation wdt:P31 wd:Q28640.
+                  OPTIONAL {{ ?occupation wdt:P425 ?field. }}
+                  SERVICE wikibase:label {{ bd:serviceParam wikibase:language "{language},en". }}
+                }}
+                LIMIT {page_size}
+                OFFSET {offset}
+                """
+
+                print(f"[Wikidata] 抓取第 {offset // page_size + 1} 頁 (offset={offset})...")
+
+                response = client.get(
+                    endpoint,
+                    params={"query": query, "format": "json"},
+                    headers={
+                        "Accept": "application/sparql-results+json",
+                        "User-Agent": "NoveltySeeking/1.0",
+                    },
+                )
+                response.raise_for_status()
+                data = response.json()
+
+                bindings = data.get("results", {}).get("bindings", [])
+                print(f"[Wikidata]   取得 {len(bindings)} 筆")
+
+                if not bindings:
+                    # 沒有更多資料了
+                    break
+
+                all_bindings.extend(bindings)
+                offset += page_size
+
+                # 如果取得的筆數少於 page_size，表示已經是最後一頁
+                if len(bindings) < page_size:
+                    break
+
+        print(f"[Wikidata] 總共取得 {len(all_bindings)} 筆原始資料")
+
+        # 解析回應
+        occupations = []
+        for item in all_bindings:
+            name = item.get("occupationLabel", {}).get("value", "")
+            field = item.get("fieldLabel", {}).get("value", "")
+
+            if name and len(name) >= 2:
+                occupations.append({
+                    "name": name,
+                    "domain": field if field else infer_domain(name, language),
+                })
+
+        # 去重
+        seen = set()
+        unique = []
+        for occ in occupations:
+            if occ["name"] not in seen:
+                seen.add(occ["name"])
+                unique.append(occ)
+
+        print(f"[Wikidata] 去重後: {len(unique)} 筆職業")
+        return unique
+
+    except Exception as e:
+        print(f"[Wikidata] 錯誤: {e}")
+        raise
+
+
+def fetch_conceptnet_occupations(language: str) -> List[dict]:
+    """
+    從 ConceptNet API 抓取職業相關概念（使用分頁）
+
+    Args:
+        language: 語言代碼 (zh/en)
+
+    Returns:
+        職業列表 [{"name": "...", "domain": "..."}, ...]
+    """
+    print(f"[ConceptNet] 正在抓取 {language} 職業資料（分頁模式）...")
+
+    endpoint = "https://api.conceptnet.io"
+    lang_code = language
+    page_size = 100  # ConceptNet 建議的 limit
+
+    # 起始概念
+    start_concepts = {
+        "zh": ["/c/zh/職業", "/c/zh/專業", "/c/zh/工作", "/c/zh/職務"],
+        "en": ["/c/en/occupation", "/c/en/profession", "/c/en/job", "/c/en/career"],
+    }
+
+    # 要查詢的關係類型
+    relations = ["/r/IsA", "/r/RelatedTo", "/r/HasA", "/r/AtLocation"]
+
+    all_occupations = []
+
+    try:
+        with httpx.Client(timeout=60.0) as client:
+            for concept in start_concepts.get(lang_code, start_concepts["zh"]):
+                for rel in relations:
+                    offset = 0
+                    max_pages = 5  # 每個組合最多抓 5 頁
+
+                    for page in range(max_pages):
+                        try:
+                            print(f"[ConceptNet] 查詢 {concept} {rel} (offset={offset})...")
+
+                            # 查詢 start 參數
+                            response = client.get(
+                                f"{endpoint}/query",
+                                params={
+                                    "start": concept,
+                                    "rel": rel,
+                                    "limit": page_size,
+                                    "offset": offset,
+                                },
+                            )
+
+                            if response.status_code != 200:
+                                print(f"[ConceptNet]   HTTP {response.status_code}, 跳過")
+                                break
+
+                            data = response.json()
+                            edges = data.get("edges", [])
+
+                            if not edges:
+                                break
+
+                            parsed = parse_conceptnet_response(data, lang_code)
+                            all_occupations.extend(parsed)
+                            print(f"[ConceptNet]   取得 {len(parsed)} 筆")
+
+                            if len(edges) < page_size:
+                                break
+
+                            offset += page_size
+
+                        except Exception as e:
+                            print(f"[ConceptNet]   錯誤: {e}")
+                            break
+
+        # 去重
+        seen = set()
+        unique = []
+        for occ in all_occupations:
+            if occ["name"] not in seen:
+                seen.add(occ["name"])
+                unique.append(occ)
+
+        print(f"[ConceptNet] 去重後: {len(unique)} 筆概念")
+        return unique
+
+    except Exception as e:
+        print(f"[ConceptNet] 錯誤: {e}")
+        raise
+
+
+def parse_conceptnet_response(data: dict, lang_code: str) -> List[dict]:
+    """解析 ConceptNet API 回應"""
+    results = []
+    edges = data.get("edges", [])
+
+    for edge in edges:
+        start = edge.get("start", {})
+        end = edge.get("end", {})
+
+        # 嘗試從兩端取得有意義的概念
+        for node in [start, end]:
+            node_id = node.get("@id", "")
+            label = node.get("label", "")
+
+            # 過濾：確保是目標語言且有意義
+            if f"/c/{lang_code}/" in node_id and label and len(label) >= 2:
+                # 排除過於泛用的詞
+                if label not in ["職業", "工作", "專業", "occupation", "job", "profession"]:
+                    results.append({
+                        "name": label,
+                        "domain": infer_domain(label, lang_code),
+                    })
+
+    return results
+
+
+def infer_domain(occupation_name: str, language: str) -> str:
+    """根據職業名稱推斷領域"""
+    if language == "zh":
+        domain_keywords = {
+            "醫": "醫療健康",
+            "護": "醫療健康",
+            "藥": "醫療健康",
+            "師": "專業服務",
+            "工程": "工程技術",
+            "技術": "工程技術",
+            "設計": "設計創意",
+            "藝術": "藝術文化",
+            "音樂": "藝術文化",
+            "運動": "體育運動",
+            "農": "農業",
+            "漁": "漁業",
+            "商": "商業貿易",
+            "銷": "商業貿易",
+            "法": "法律",
+            "律": "法律",
+            "教": "教育",
+            "研究": "學術研究",
+            "科學": "學術研究",
+            "廚": "餐飲服務",
+            "烹": "餐飲服務",
+            "建築": "建築營造",
+            "軍": "軍事國防",
+            "警": "公共安全",
+            "消防": "公共安全",
+            "記者": "媒體傳播",
+            "編輯": "媒體傳播",
+            "作家": "文學創作",
+            "程式": "資訊科技",
+            "軟體": "資訊科技",
+            "電腦": "資訊科技",
+        }
+    else:
+        domain_keywords = {
+            "doctor": "Healthcare",
+            "nurse": "Healthcare",
+            "medical": "Healthcare",
+            "engineer": "Engineering",
+            "technical": "Engineering",
+            "design": "Design & Creative",
+            "artist": "Arts & Culture",
+            "music": "Arts & Culture",
+            "sport": "Sports",
+            "athletic": "Sports",
+            "farm": "Agriculture",
+            "fish": "Fishery",
+            "business": "Business",
+            "sales": "Business",
+            "law": "Legal",
+            "attorney": "Legal",
+            "teach": "Education",
+            "professor": "Education",
+            "research": "Academic Research",
+            "scien": "Academic Research",
+            "chef": "Culinary",
+            "cook": "Culinary",
+            "architect": "Architecture",
+            "military": "Military",
+            "police": "Public Safety",
+            "fire": "Public Safety",
+            "journal": "Media",
+            "editor": "Media",
+            "writer": "Literature",
+            "author": "Literature",
+            "program": "Information Technology",
+            "software": "Information Technology",
+            "computer": "Information Technology",
+            "develop": "Information Technology",
+        }
+
+    name_lower = occupation_name.lower()
+    for keyword, domain in domain_keywords.items():
+        if keyword in name_lower:
+            return domain
+
+    return "專業領域" if language == "zh" else "Professional Field"
+
+
+def save_json(data: List[dict], source: str, language: str) -> None:
+    """儲存資料到 JSON 檔案"""
+    filename = f"{source}_occupations_{language}.json"
+    filepath = DATA_DIR / filename
+
+    output = {
+        "metadata": {
+            "source": source,
+            "language": language,
+            "fetched_at": datetime.now(timezone.utc).isoformat(),
+            "total_count": len(data),
+        },
+        "occupations": data,
+    }
+
+    with open(filepath, "w", encoding="utf-8") as f:
+        json.dump(output, f, ensure_ascii=False, indent=2)
+
+    print(f"[儲存] {filepath} ({len(data)} 筆)")
+
+
+def main():
+    """主程式"""
+    print("=" * 60)
+    print("職業資料抓取腳本")
+    print(f"輸出目錄: {DATA_DIR}")
+    print("=" * 60)
+    print()
+
+    # 確保輸出目錄存在
+    DATA_DIR.mkdir(parents=True, exist_ok=True)
+
+    # 抓取 Wikidata
+    print("--- Wikidata ---")
+    try:
+        wikidata_zh = fetch_wikidata_occupations("zh")
+        save_json(wikidata_zh, "wikidata", "zh")
+    except Exception as e:
+        print(f"Wikidata 中文抓取失敗: {e}")
+        wikidata_zh = []
+
+    try:
+        wikidata_en = fetch_wikidata_occupations("en")
+        save_json(wikidata_en, "wikidata", "en")
+    except Exception as e:
+        print(f"Wikidata 英文抓取失敗: {e}")
+        wikidata_en = []
+
+    print()
+
+    # 抓取 ConceptNet
+    print("--- ConceptNet ---")
+    try:
+        conceptnet_zh = fetch_conceptnet_occupations("zh")
+        save_json(conceptnet_zh, "conceptnet", "zh")
+    except Exception as e:
+        print(f"ConceptNet 中文抓取失敗: {e}")
+        conceptnet_zh = []
+
+    try:
+        conceptnet_en = fetch_conceptnet_occupations("en")
+        save_json(conceptnet_en, "conceptnet", "en")
+    except Exception as e:
+        print(f"ConceptNet 英文抓取失敗: {e}")
+        conceptnet_en = []
+
+    print()
+    print("=" * 60)
+    print("抓取完成！")
+    print(f"  Wikidata 中文: {len(wikidata_zh)} 筆")
+    print(f"  Wikidata 英文: {len(wikidata_en)} 筆")
+    print(f"  ConceptNet 中文: {len(conceptnet_zh)} 筆")
+    print(f"  ConceptNet 英文: {len(conceptnet_en)} 筆")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()

frontend/src/components/TransformationInputPanel.tsx

@@ -7,8 +7,9 @@ const { Title, Text } = Typography;
 
 const EXPERT_SOURCE_OPTIONS = [
   { label: 'LLM 生成', value: 'llm' as ExpertSource, description: '使用 AI 模型生成專家' },
-  { label: 'Wikidata', value: 'wikidata' as ExpertSource, description: '從維基數據查詢職業' },
+  { label: '精選職業', value: 'curated' as ExpertSource, description: '從 210 個常見職業隨機選取（含具體領域）' },
-  { label: 'ConceptNet', value: 'conceptnet' as ExpertSource, description: '從知識圖譜查詢概念' },
+  { label: 'DBpedia', value: 'dbpedia' as ExpertSource, description: '從 DBpedia 隨機選取職業 (2164 筆)' },
+  { label: 'Wikidata', value: 'wikidata' as ExpertSource, description: '從 Wikidata 查詢職業 (需等待 API)' },
 ];
 
 interface TransformationInputPanelProps {

frontend/src/hooks/useExpertTransformation.ts

@@ -155,7 +155,7 @@ export function useExpertTransformation(options: UseExpertTransformationOptions
         });
       });
     },
-    [options.model, options.temperature]
+    [options.model, options.temperature, options.expertSource]
   );
 
   const transformAll = useCallback(

frontend/src/types/index.ts

@@ -230,7 +230,7 @@ export interface ExpertTransformationDAGResult {
   results: ExpertTransformationCategoryResult[];
 }
 
-export type ExpertSource = 'llm' | 'wikidata' | 'conceptnet';
+export type ExpertSource = 'llm' | 'curated' | 'dbpedia' | 'wikidata';
 
 export interface ExpertTransformationRequest {
   query: string;
@@ -240,7 +240,7 @@ export interface ExpertTransformationRequest {
   keywords_per_expert: number;       // 1-3
   custom_experts?: string[];         // ["藥師", "工程師"]
   expert_source?: ExpertSource;      // 專家來源 (default: 'llm')
-  expert_language?: string;          // 外部來源語言 (default: 'zh')
+  expert_language?: string;          // 外部來源語言 (default: 'en')
   model?: string;
   temperature?: number;
 }