Beyond Predefined Schemas: TRACE-KG for Context-Enriched Knowledge Graphs from Complex Documents

• URL: http://arxiv.org/abs/2604.03496v1
• Date: Fri, 03 Apr 2026 22:41:35 GMT
• Title: Beyond Predefined Schemas: TRACE-KG for Context-Enriched Knowledge Graphs from Complex Documents
• Abstract summary: TRACE-KG (Text-diven schemA for Context-Enriched Knowledge Graphs) is a framework that jointly constructs a context-enriched knowledge graph and an induced schema.<n> experiments show that TRACE-KG produces structurally coherent, traceable knowledge graphs.
• Score: 12.445872440074114
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge graph construction 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 TRACE-KG (Text-dRiven schemA for Context-Enriched Knowledge Graphs), a multimodal 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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