Beyond Predefined Schemas: TRACE-KG for Context-Enriched Knowledge Graph Generation
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 r...
| Authors | Mohammad Sadeq Abolhasani, Yang Ba, Yixuan He et al. |
|---|---|
| Primary category | cs.AI |
| Published | 3 April 2026 |
| Last updated at source | 15 June 2026 |
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