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KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion

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arxiv 2402.02389 v2 pith:OCLVKJVT submitted 2024-02-04 cs.CL cs.AI

Yanbin Wei , Qiushi Huang , James T. Kwok , Yu Zhang This is my paper · ORCID
classification cs.CLcs.AI
keywords knowledgekicgptgraphlanguagetrainingtriple-basedalleviatecompletion
verification ladder T0 review T1 audit T2 compute T3 formal
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Knowledge Graph Completion (KGC) is crucial for addressing knowledge graph incompleteness and supporting downstream applications. Many models have been proposed for KGC. They can be categorized into two main classes: triple-based and text-based approaches. Triple-based methods struggle with long-tail entities due to limited structural information and imbalanced entity distributions. Text-based methods alleviate this issue but require costly training for language models and specific finetuning for knowledge graphs, which limits their efficiency. To alleviate these limitations, in this paper, we propose KICGPT, a framework that integrates a large language model (LLM) and a triple-based KGC retriever. It alleviates the long-tail problem without incurring additional training overhead. KICGPT uses an in-context learning strategy called Knowledge Prompt, which encodes structural knowledge into demonstrations to guide the LLM. Empirical results on benchmark datasets demonstrate the effectiveness of KICGPT with smaller training overhead and no finetuning.

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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. 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. ViSR-KGC: Visual Subgraph Reasoning with Vision-Language Models for Multimodal Knowledge Graph Completion

    cs.AI 2026-08 reject novelty 5.0 of 10

    ViSR-KGC combines multimodal embeddings, query-aware subgraph extraction, and graph-to-image rendering to let a vision-language model perform knowledge graph link prediction.

  4. SLiNT: Structure-aware Language Model with Injection and Contrastive Training for Knowledge Graph Completion

    cs.CL 2025-09 conditional novelty 4.0 of 10

    A structure-injected frozen-LLM framework with contrastive training achieves small gains on WN18RR and FB15k-237 link prediction.

  5. 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.

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