Problem
Research questions and friction points this paper is trying to address.
knowledge graph construction
predefined ontology
schema-free extraction
context-dependent information
technical documents
Beyond Predefined Schemas: TRACE-KG for Context-Enriched Knowledge Graphs from Complex Documents
🤖 AI Summary
Traditional knowledge graph construction methods struggle to balance structural consistency and contextual richness, as they are either constrained by the high cost of predefined ontologies or suffer from fragmentation due to schema-free extraction. This work proposes TRACE-KG, a novel framework that, for the first time, jointly generates a knowledge graph and a data-driven semantic schema without requiring any pre-specified ontology. The approach leverages multimodal joint modeling and structured qualifiers to represent conditional relationships, enabling end-to-end traceable knowledge extraction. Experimental results on complex technical documents demonstrate that the generated graphs significantly outperform existing ontology-driven and schema-free methods in terms of structural coherence, semantic richness, and traceability back to the source documents.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
schema induction
context-enriched knowledge graph
traceability
ontology-free
structured qualifiers
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