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>

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