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

View PDF HTML (experimental) 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. Subjects: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG) Cite as: arXiv:2604.03496 [cs.AI] (or arXiv:2604.03496v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2604.03496 arXiv-issued DOI via DataCite (pending registration) Submission history From: Mohammad Sadeq Abolhasani [view email] [v1] Fri, 3 Apr 2026 22:41:35 UTC (15,415 KB)