SIKGC: Structural Information Prompt Based Knowledge Graph Completion with Large Language Models

Knowledge Graph Completion (KGC) aims to enrich and complete the knowledge graph by discovering missing information from existing fact triples. However, existing KGC methods often overlook the utilization of structured knowledge within the knowledge base. In this paper, we propose a novel Large Language Models-based Knowledge Graph Completion framework, called SIKGC, which builds the structural information prompt to assist the knowledge graph completion tasks. Specifically, we arrange the triples in the knowledge graph as the sequences of text. By fusing the descriptions of entities, relations and their structural information as task-aware prompts, we input such prompts into large language models and regard the responses as prediction tasks. The experimental results on various public datasets show that the proposed method outperforms all baseline methods for the three knowledge completion tasks and attains state-of-the-art in triple classification. We also demonstrate that fine-tuning the smaller large language models (e.g., Baichuan2-13B, LLaMA2-13B, ChatGLM3-6B) with relevant data markedly enhances their KGC capabilities and significantly outperforms GPT-4.