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Large Language Models and Knowledge Graphs: Opportunities and Challenges

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arxiv 2308.06374 v1 pith:Z5PWAJFC submitted 2023-08-11 cs.AI cs.CL

Jeff Z. Pan , Simon Razniewski , Jan-Christoph Kalo , Sneha Singhania , Jiaoyan Chen , Stefan Dietze , Hajira Jabeen , Janna Omeliyanenko
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Wen Zhang Matteo Lissandrini Russa Biswas Gerard de Melo Angela Bonifati Edlira Vakaj Mauro Dragoni Damien Graux
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classification cs.AIcs.CL
keywords knowledgeexplicitrepresentationchallengesfocusgraphslanguagelarge
verification ladder T0 review T1 audit T2 compute T3 formal
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Large Language Models (LLMs) have taken Knowledge Representation -- and the world -- by storm. This inflection point marks a shift from explicit knowledge representation to a renewed focus on the hybrid representation of both explicit knowledge and parametric knowledge. In this position paper, we will discuss some of the common debate points within the community on LLMs (parametric knowledge) and Knowledge Graphs (explicit knowledge) and speculate on opportunities and visions that the renewed focus brings, as well as related research topics and challenges.

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Cited by 4 Pith papers

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

  1. FAIR GraphRAG: A Retrieval-Augmented Generation Approach for Semantic Data Analysis

    cs.IR 2026-07 conditional novelty 6.0 of 10

    Embedding FAIR Digital Objects as graph nodes yields a GraphRAG system that measurably improves accuracy, coverage and explainability on biomedical RNA-seq queries versus a non-FAIR baseline.

  2. VeriLLMed: Interactive Visual Debugging of Medical Large Language Models with Knowledge Graphs

    cs.CL 2026-04 unverdicted novelty 6.0 of 10

    VeriLLMed uses biomedical knowledge graphs to turn medical LLM reasoning into comparable paths and automatically flags three recurring error types: relation, branch, and missing errors.

  3. Leveraging Knowledge Graphs and LLM Reasoning to Identify Operational Bottlenecks for Warehouse Planning Assistance

    cs.LG 2025-07 conditional novelty 6.0 of 10

    An LLM agent querying a knowledge graph built from discrete-event simulation output identified warehouse bottlenecks and answered operational questions more reliably than single-pass query baselines.

  4. Neuro-Symbolic Strong-AI Robots with Closed Knowledge Assumption: Learning and Deductions

    cs.LO 2026-02 unverdicted novelty 4.0 of 10

    AGI robots learn and deduce using Belnap's 4-valued bilattice and Closed Knowledge Assumption to expand knowledge while supporting inconsistencies and providing logical security.

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