Kavli Affiliate: Yi Zhou
| First 5 Authors: Rui Yang, Jiahao Zhu, Jianping Man, Hongze Liu, Li Fang
| Summary:
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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