Exploring Multi-aspect Information for Knowledge Graph Completion with Large Language Model

Linghui Wang, Jiawei Sheng, Wenyuan Zhang, Tingwen Liu, Chuang Zhang

Abstract: Knowledge Graph Completion (KGC) is important in addressing the incompleteness of Knowledge Graphs (KGs) and supporting intelligent infrastructures. Recently, numerous methods have been developed for the utilization of Large Language Models (LLMs) to complete KGs in a textual generation manner. However, integrating KGs with LLMs presents multiple challenges. First, the output of LLMs is often unconstrained, and directly performing KGC with LLMs may generate entities beyond the scope of KGs and suffer from hallucination. Second, the inherent token length limitation of LLMs may hinder the integration of multi-aspect information, thereby restricting the effectiveness and efficiency in inference. Third, existing methods that integrate LLMs with KGs typically leverage partial aspects of KG contexts, overlooking the crucial role of multi-aspect information in prompting KGC. In fact, there exists crucial multi-aspect information of KG contexts that support the correctness of a factual triple, such as entity/relation descriptions, reasoning paths and entity neighbors. To this end, we propose a novel framework to explore the impact of multi-aspect information of KG contexts for KGC, termed as MAKGC. Particularly, given a target incomplete triple, MAKGC generates a list of candidate entities using an embedding model, incorporates the most relevant relation paths and entity descriptions as embeddings, and integrates them with structural embeddings into a set of instructions. In this way, the multi-aspect information facilitate LLMs in making accurate predictions. Extensive experiments demonstrate the effectiveness on benchmark datasets, and our model outperform previous competitive methods.

External IDs:dblp:conf/ijcnn/WangSZLZ25