用本体知识增强大模型，让知识图谱补全更准确
Ontology-Enhanced Knowledge Graph Completion using Large Language Models
- 将图结构信息转为文本，结合本体知识引导大模型推理
- 在三个基准数据集上超越现有方法，最高提升12.3%
- 适合需要可解释推理的知识图谱研究者
大语言模型（LLM）在知识图谱补全（KGC）中广泛应用，但其作为黑箱模型，依赖隐式知识表示和并行传播错误信息，难以产生确定性推理。本文提出一种基于本体增强的LLM-KGC方法（OL-KGC），首先利用神经感知机制将结构信息嵌入文本空间，再通过自动化提取算法从待补全的知识图谱中获取本体知识，并转化为大模型可理解的文本形式，提供逻辑指导。在FB15K-237、UMLS和WN18RR三个主流基准上进行实验，结果表明OL-KGC在多个评估指标上显著优于现有主流方法，达到当前最优性能。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have been extensively adopted in Knowledge Graph Completion (KGC), showcasing significant research advancements. However, as black-box models driven by deep neural architectures, current LLM-based KGC methods rely on implicit knowledge representation with parallel propagation of erroneous knowledge, thereby hindering their ability to produce conclusive and decisive reasoning outcomes. We aim to integrate neural-perceptual structural information with ontological knowledge, leveraging the powerful capabilities of LLMs to achieve a deeper understanding of the intrinsic logic of the knowledge. We propose an ontology enhanced KGC method using LLMs -- OL-KGC. It first leverages neural perceptual mechanisms to effectively embed structural information into the textual space, and then uses an automated extraction algorithm to retrieve ontological knowledge from the knowledge graphs (KGs) that needs to be completed, which is further transformed into a textual format comprehensible to LLMs for providing logic guidance. We conducted extensive experiments on three widely-used benchmarks -- FB15K-237, UMLS and WN18RR. The experimental results demonstrate that OL-KGC significantly outperforms existing mainstream KGC methods across multiple evaluation metrics, achieving state-of-the-art performance.
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