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

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 prac

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Mohammad Sadeq Abolhasani, Yang Ba, Yixuan He, Rong Pan · · 1 min read · 17 views
#cs.AI #cs.IR #cs.LG

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.

Executive Summary

The article introduces TRACE-KG, a novel multimodal framework for constructing context-enriched knowledge graphs (KGs) from complex documents without relying on predefined ontologies. Addressing limitations in traditional ontology-driven and schema-free approaches, TRACE-KG leverages a text-driven, data-driven schema to capture conditional relations through structured qualifiers while ensuring traceability to source evidence. The framework dynamically organizes entities and relations into a reusable semantic scaffold, demonstrating superior structural coherence compared to existing pipelines. Experimental validation underscores its potential as a practical alternative for KG construction in domains with dense, context-dependent technical information.

Key Points

  • ▸ TRACE-KG eliminates the need for costly predefined ontologies by inducing a schema dynamically from text, addressing scalability and maintenance challenges in ontology-driven KG construction.
  • ▸ The framework employs structured qualifiers to capture conditional relations, enhancing the granularity and contextual depth of the knowledge graph beyond traditional schema-free methods.
  • ▸ Experiments validate TRACE-KG’s ability to produce structurally coherent, traceable KGs, offering a balanced solution between rigid ontologies and fragmented, schema-free outputs.

Merits

Innovative Hybrid Approach

TRACE-KG bridges the gap between ontology-driven and schema-free KG construction by combining the traceability and coherence of predefined schemas with the flexibility of data-driven induction.

Contextual Depth and Traceability

The use of structured qualifiers and dynamic schema induction ensures that relations are contextually enriched and fully traceable to source evidence, addressing a critical gap in schema-free methods.

Scalability and Practicality

By eliminating the need for costly schema design and maintenance, TRACE-KG offers a scalable solution for domains with evolving or complex information structures, such as technical or scientific documents.

Demerits

Dependence on Text Quality

TRACE-KG’s performance may be sensitive to the quality and consistency of the input text, particularly in documents with ambiguous or poorly structured language, which could impact the accuracy of schema induction and relation extraction.

Computational Overhead

The dynamic induction of schemas and processing of structured qualifiers may introduce additional computational complexity compared to simpler schema-free methods, potentially limiting scalability for very large datasets.

Validation Challenges

While experiments demonstrate structural coherence, the framework’s effectiveness in real-world applications may require broader validation across diverse domains to ensure generalizability and robustness.

Expert Commentary

TRACE-KG represents a significant advancement in the field of knowledge graph construction by addressing a longstanding tension between the rigidity of ontology-driven approaches and the chaos of schema-free extraction. The framework’s innovation lies in its ability to induce a reusable semantic scaffold dynamically, which not only preserves traceability but also enhances the contextual richness of the resulting KG. This dual capability is particularly valuable in domains where information is dense, context-dependent, and subject to frequent updates, such as scientific literature or legal texts. However, the framework’s reliance on high-quality input text and the potential computational overhead of dynamic schema induction may pose challenges in practice. Future work should explore hybrid models that combine TRACE-KG’s strengths with lightweight preprocessing techniques to mitigate these limitations. Additionally, the framework’s generalizability across diverse domains remains an open question, warranting further empirical validation. Overall, TRACE-KG sets a new direction for knowledge graph construction, one that prioritizes adaptability and traceability without sacrificing structural coherence.

Recommendations

  • ✓ Conduct further empirical validation of TRACE-KG across a broader range of domains, including low-resource languages and highly unstructured documents, to assess its generalizability and robustness.
  • ✓ Explore integration with lightweight preprocessing tools or pre-trained language models to address potential computational overhead and improve performance in real-time applications.
  • ✓ Develop standardized benchmarks for evaluating traceability and contextual depth in knowledge graphs, enabling more objective comparisons with existing frameworks and facilitating adoption in regulated industries.
  • ✓ Investigate the feasibility of incorporating user feedback loops into TRACE-KG to refine schema induction and relation extraction iteratively, enhancing adaptability in dynamic environments.

Sources

Original: arXiv - cs.AI

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