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Exploring Large Language Models for Knowledge Graph Completion

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arxiv 2308.13916 v5 pith:JWCOWYFE submitted 2023-08-26 cs.CL cs.AI

Liang Yao , Jiazhen Peng , Chengsheng Mao , Yuan Luo This is my paper · ORCID
classification cs.CLcs.AI
keywords knowledgegraphgraphsmodelscompletionlanguagelargerelation
verification ladder T0 review T1 audit T2 compute T3 formal
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Knowledge graphs play a vital role in numerous artificial intelligence tasks, yet they frequently face the issue of incompleteness. In this study, we explore utilizing Large Language Models (LLM) for knowledge graph completion. We consider triples in knowledge graphs as text sequences and introduce an innovative framework called Knowledge Graph LLM (KG-LLM) to model these triples. Our technique employs entity and relation descriptions of a triple as prompts and utilizes the response for predictions. Experiments on various benchmark knowledge graphs demonstrate that our method attains state-of-the-art performance in tasks such as triple classification and relation prediction. We also find that fine-tuning relatively smaller models (e.g., LLaMA-7B, ChatGLM-6B) outperforms recent ChatGPT and GPT-4.

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Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 24 citations worldwide. Full citation record

  1. Enhancing Large Language Model for Knowledge Graph Completion via Structure-Aware Alignment-Tuning

    cs.CL 2025-09 reject novelty 6.0 of 10

    SAT uses hierarchical contrastive alignment and a unified graph instruction to tune a lightweight adapter for knowledge graph completion, reporting large link prediction gains.

  2. K-ON: Stacking Knowledge On the Head Layer of Large Language Model

    cs.CL 2025-02 conditional novelty 6.0 of 10

    K-ON stacks K prediction heads onto an LLM to generate entity tokens in one step and uses entity-level contrastive learning, achieving new state-of-the-art results on two knowledge graph completion benchmarks.

  3. ReaLM: Residual Quantization Bridging Knowledge Graph Embeddings and Large Language Models

    cs.CL 2025-10 conditional novelty 4.0 of 10

    Using residual quantization to represent KG entities as code tokens lets an LLM do link prediction and reach reported state-of-the-art MRR on WN18RR (0.608) and FB15k-237 (0.467) when ontology constraints are added.

  4. KG-BiLM: Knowledge Graph Embedding via Bidirectional Language Models

    cs.CL 2025-06 conditional novelty 4.0 of 10

    A bidirectional decoder with a graph-aware attention mask, knowledge-masked prediction, and contrastive sub-graph alignment achieves reported state-of-the-art link prediction on Wikidata5M and competitive results on WN18RR.

  5. Evo-DKD: Dual-Knowledge Decoding for Autonomous Ontology Evolution in Large Language Models

    cs.AI 2025-07 reject novelty 2.0 of 10

    A proposed dual-decoder LLM for autonomous ontology evolution is described but never implemented; only a prompt-based simulation with a 1.1B model on 120 evaluation examples is tested.

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