GS-KGC: A Generative Subgraph-based Framework for Knowledge Graph Completion with Large Language Models

• URL: http://arxiv.org/abs/2408.10819v2
• Date: Fri, 03 Jan 2025 04:12:32 GMT
• Title: GS-KGC: A Generative Subgraph-based Framework for Knowledge Graph Completion with Large Language Models
• Authors: Rui Yang, Jiahao Zhu, Jianping Man, Hongze Liu, Li Fang, Yi Zhou,
• Abstract summary: We propose a novel completion framework called textbfGenerative textbfSubgraph-based KGC (GS-KGC) This framework primarily includes a subgraph partitioning algorithm designed to generate negatives and neighbors. Experiments conducted on four common KGC datasets highlight the advantages of the proposed GS-KGC.
• Score: 7.995716933782121
• License:
• Abstract: Knowledge graph completion (KGC) focuses on identifying missing triples in a knowledge graph (KG) , which is crucial for many downstream applications. Given the rapid development of large language models (LLMs), some LLM-based methods are proposed for KGC task. However, most of them focus on prompt engineering while overlooking the fact that finer-grained subgraph information can aid LLMs in generating more accurate answers. In this paper, we propose a novel completion framework called \textbf{G}enerative \textbf{S}ubgraph-based KGC (GS-KGC), which utilizes subgraph information as contextual reasoning and employs a QA approach to achieve the KGC task. This framework primarily includes a subgraph partitioning algorithm designed to generate negatives and neighbors. Specifically, negatives can encourage LLMs to generate a broader range of answers, while neighbors provide additional contextual insights for LLM reasoning. Furthermore, we found that GS-KGC can discover potential triples within the KGs and new facts beyond the KGs. Experiments conducted on four common KGC datasets highlight the advantages of the proposed GS-KGC, e.g., it shows a 5.6\% increase in Hits@3 compared to the LLM-based model CP-KGC on the FB15k-237N, and a 9.3\% increase over the LLM-based model TECHS on the ICEWS14.

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