[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-knowledge-graph":3},{"tag":4,"articles":11},{"id":5,"name":6,"slug":7,"article_count":8,"description_zh":9,"description_en":10},"20d32618-b8be-4487-9b35-a626651cc3d9","knowledge graph","knowledge-graph",3,"知識圖譜把分散的實體、關係與規則連成可查詢的結構，常用在製造業解釋型 AI、文獻探索、推薦與資料整合。它的價值在於讓 LLM 或分析系統先對齊事實，再生成更可追溯的結果。","Knowledge graphs model entities, relations, and rules in a queryable structure. They matter for explainable ML, research discovery, recommendation, and data integration because they give LLMs and analytics systems a factual layer to ground results and trace sources.",[12,21,29,36],{"id":13,"slug":14,"title":15,"summary":16,"category":17,"image_url":18,"cover_image":18,"language":19,"created_at":20},"b3cf03a7-5c6d-4627-a04e-9a2f74d67fea","5-patterns-graph-enhanced-rag-production-zh","5 個生產級 Graph RAG 模式","5 種 Graph RAG 模式，幫你判斷何時用 SQL、向量與圖譜一起回答風險與依賴問題。","industry","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779510964912-prd4.png","zh","2026-05-23T04:35:39.304624+00:00",{"id":22,"slug":23,"title":24,"summary":25,"category":26,"image_url":27,"cover_image":27,"language":19,"created_at":28},"acaa0a72-4a72-44c1-b290-b9fde291c56c","conformal-path-reasoning-kgqa-calibration-zh","CPR 讓 KGQA 更可控","CPR 把 conformal calibration 放到 KGQA 的推理路徑層級，目標是讓答案集合更小、覆蓋率更穩定，提升可部署性。","research","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778481062254-mx5m.png","2026-05-11T06:30:34.919508+00:00",{"id":30,"slug":31,"title":32,"summary":33,"category":26,"image_url":34,"cover_image":34,"language":19,"created_at":35},"2ce35a50-85a1-42b5-8d74-af24ccaaf716","llms-knowledge-graphs-ml-explainability-zh","知識圖譜加 LLM 讓製造業 XAI 更好懂","這篇論文把知識圖譜和 LLM 接起來，讓製造業的機器學習結果能被轉成更好懂的解釋。重點不是亂編答案，而是先抓相關圖譜事實，再交給語言模型整理。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1776665388382-t6m1.png","2026-04-20T06:09:32.525811+00:00",{"id":37,"slug":38,"title":39,"summary":40,"category":26,"image_url":41,"cover_image":41,"language":19,"created_at":42},"98422b28-f1f4-4453-b6f6-3eac1e1c899c","paper-circle-multi-agent-research-discovery-zh","Paper Circle 用多代理 LLM 做研究探索","Paper Circle 用多代理 LLM 把找論文、排序、整理到知識圖譜分析串成流程，目標是讓文獻探索更可重現，也更容易整合進工具。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775628233980-5qlq.png","2026-04-08T06:03:34.360477+00:00"]