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KGValidator: A Framework for Automatic Validation of Knowledge Graph Construction

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arxiv 2404.15923 v1 pith:LNTHBXQK submitted 2024-04-24 cs.AI cs.CL

Jack Boylan , Shashank Mangla , Dominic Thorn , Demian Gholipour Ghalandari , Parsa Ghaffari , Chris Hokamp This is my paper
classification cs.AIcs.CL
keywords knowledgevalidationframeworkgenerativemodelsautomaticexternalgraph
verification ladder T0 review T1 audit T2 compute T3 formal
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This study explores the use of Large Language Models (LLMs) for automatic evaluation of knowledge graph (KG) completion models. Historically, validating information in KGs has been a challenging task, requiring large-scale human annotation at prohibitive cost. With the emergence of general-purpose generative AI and LLMs, it is now plausible that human-in-the-loop validation could be replaced by a generative agent. We introduce a framework for consistency and validation when using generative models to validate knowledge graphs. Our framework is based upon recent open-source developments for structural and semantic validation of LLM outputs, and upon flexible approaches to fact checking and verification, supported by the capacity to reference external knowledge sources of any kind. The design is easy to adapt and extend, and can be used to verify any kind of graph-structured data through a combination of model-intrinsic knowledge, user-supplied context, and agents capable of external knowledge retrieval.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Knowledge Conceptualization Impacts RAG Efficacy

    cs.AI 2025-07 conditional novelty 6.0 of 10

    An empirical study showing that both schema complexity and representation format affect how well GPT-4o generates SPARQL queries from competency questions, with mixed results across two knowledge graph families.

  2. A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data

    cs.AI 2026-01 conditional novelty 5.0 of 10

    A metric-oriented survey that classifies intrinsic quality and trustworthiness metrics for LLM-generated data across six modalities and documents systematic evaluation gaps in the current literature.

  3. MedKGent: A Large Language Model Agent Framework for Constructing Temporally Evolving Medical Knowledge Graph

    cs.CL 2025-08 reject novelty 4.0 of 10

    A submission whose abstract describes a large temporal medical knowledge graph built by LLM agents, but whose full text is an unrelated paper on histogram regression, leaving the announced claims unsupported.

  4. AI4Research: A Survey of Artificial Intelligence for Scientific Research

    cs.CL 2025-07 conditional novelty 4.0 of 10

    A survey that organizes AI-for-research work into five tasks, comprehension, survey, discovery, writing, and peer review, and compiles associated tools and benchmarks.

  5. MultiCNKG: Integrating Cognitive Neuroscience, Gene, and Disease Knowledge Graphs Using Large Language Models

    cs.AI 2025-10 reject novelty 3.0 of 10

    An LLM merges three biomedical ontologies into a small knowledge graph, but its validation metrics are self-contradictory and the resource is not released.

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