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Relations Prediction for Knowledge Graph Completion using Large Language Models

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arxiv 2405.02738 v1 pith:7YYIRSVN submitted 2024-05-04 cs.CL cs.AI

Sakher Khalil Alqaaidi , Krzysztof Kochut This is my paper · ORCID
classification cs.CLcs.AI
keywords knowledgegraphlargepredictioncompletiongraphslanguagemodel
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Knowledge Graphs have been widely used to represent facts in a structured format. Due to their large scale applications, knowledge graphs suffer from being incomplete. The relation prediction task obtains knowledge graph completion by assigning one or more possible relations to each pair of nodes. In this work, we make use of the knowledge graph node names to fine-tune a large language model for the relation prediction task. By utilizing the node names only we enable our model to operate sufficiently in the inductive settings. Our experiments show that we accomplish new scores on a widely used knowledge graph benchmark.

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Cited by 1 Pith paper

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  1. Large Language Models for Knowledge Graph Embedding: A Survey

    cs.CL 2025-01 reject novelty 3.0 of 10

    A survey that classifies LLM-based knowledge graph embedding methods by knowledge graph scenario and degree of LLM invocation, but with no new experimental results.

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