Beyond Predefined Schemas: TRACE-KG for Context-Enriched Knowledge Graphs from Complex Documents
Summary: arXiv:2604.03496v1 Announce Type: new
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.
Introduction
The emergence of knowledge graphs has transformed the way information is stored and accessed. Traditional methods of knowledge graph construction have encountered significant challenges, particularly when dealing with complex documents that contain dense and context-dependent information. The reliance on predefined ontologies can lead to rigid structures that are costly to maintain, while schema-free methods often result in fragmented representations lacking coherence.
TRACE-KG Framework
TRACE-KG addresses these limitations by providing a novel approach to knowledge graph construction. This framework is designed to create a context-enriched knowledge graph that is both adaptable and traceable. Below are the key features of TRACE-KG:
- Data-Driven Schema: Instead of relying on predefined ontologies, TRACE-KG uses a schema that evolves from the data itself, allowing for greater flexibility and relevance to the specific context of the documents being analyzed.
- Contextual Understanding: The framework incorporates structured qualifiers that capture conditional relations, enhancing the ability to understand and represent complex interdependencies among entities.
- Traceability: One of the standout features of TRACE-KG is its commitment to traceability. The knowledge graph maintains connections to the original source material, enabling users to verify and understand the foundations of the extracted knowledge.
- Multimodal Integration: TRACE-KG is designed to handle various types of data inputs, ensuring that it can accommodate diverse document formats and structures.
Experimental Results
In experiments conducted to evaluate the effectiveness of TRACE-KG, significant improvements were observed in terms of structural coherence and traceability when compared to traditional methods. The knowledge graphs generated by TRACE-KG not only maintained a high level of organization but also provided users with clear pathways back to the original information sources.
Conclusion
TRACE-KG represents a significant advancement in the field of knowledge graph construction. By moving beyond the constraints of predefined schemas and leveraging a data-driven approach, TRACE-KG offers a innovative solution that enhances the understanding and organization of complex information. As knowledge graphs continue to play a critical role in various applications, frameworks like TRACE-KG will be essential in ensuring that these resources remain relevant, coherent, and valuable.