无需预设框架，自动构建可追溯的知识图谱。
Beyond Predefined Schemas: TRACE-KG for Context-Enriched Knowledge Graph Generation
- 基于文本动态生成语义结构，融合上下文信息
- 生成的图谱结构清晰且每条关系可追溯原文
- 适合长篇技术文档的智能知识提取
知识图谱生成通常依赖预定义本体或无模式抽取。前者虽保证类型一致性但需高成本设计维护，后者常导致图谱碎片化、缺乏全局组织，尤其在密集、上下文依赖强的技术文档中表现不佳。本文提出TRACE-KG（Text-driven Schema for Context-Enriched Knowledge Graphs），一种联合构建上下文增强型知识图谱与自动生成本体的框架，不依赖预设本体。该方法通过结构化限定词捕捉条件关系，利用数据驱动的本体作为可复用的语义骨架，同时保持与原始证据的完整可追溯性。实验表明，TRACE-KG能生成结构连贯、可追溯的知识图谱，为有监督与无模式构建提供了实用替代方案。
原文摘要 · Abstract (English)
Knowledge graph generation typically relies either on predefined ontologies or on schema-free extraction. Ontology-driven pipelines enforce consistent typing but require costly schema design and maintenance, whereas schema-free methods often produce fragmented graphs with weak global organization, especially in long technical documents with dense, context-dependent information. We propose \textbf{TRACE-KG} (\textbf{T}ext-d\textbf{R}iven schem\textbf{A} for \textbf{C}ontext-\textbf{E}nriched \textbf{K}nowledge \textbf{G}raphs), a framework that jointly constructs a context-enriched knowledge graph and an induced schema without assuming a predefined ontology. TRACE-KG captures conditional relations through structured qualifiers and organizes entities and relations using a data-driven schema that serves as a reusable semantic scaffold while preserving full traceability to the source evidence. Experiments show that TRACE-KG produces structurally coherent, traceable knowledge graphs and offers a practical alternative to both ontology-driven and schema-free construction pipelines.
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