KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge

TL;DR

KG-FIT introduces a novel fine-tuning approach for knowledge graph embeddings that leverages large language models to incorporate open-world knowledge, significantly improving link prediction performance.

Contribution

It proposes a new method combining LLM-guided hierarchical clustering with textual info for enhanced KG embedding fine-tuning, outperforming existing models.

Findings

Achieves up to 14.4% improvement in Hits@10 on benchmark datasets.

Demonstrates substantial performance gains over structure-based models.

Effectively integrates open-world knowledge into KG embeddings.

Abstract

Knowledge Graph Embedding (KGE) techniques are crucial in learning compact representations of entities and relations within a knowledge graph, facilitating efficient reasoning and knowledge discovery. While existing methods typically focus either on training KGE models solely based on graph structure or fine-tuning pre-trained language models with classification data in KG, KG-FIT leverages LLM-guided refinement to construct a semantically coherent hierarchical structure of entity clusters. By incorporating this hierarchical knowledge along with textual information during the fine-tuning process, KG-FIT effectively captures both global semantics from the LLM and local semantics from the KG. Extensive experiments on the benchmark datasets FB15K-237, YAGO3-10, and PrimeKG demonstrate the superiority of KG-FIT over state-of-the-art pre-trained language model-based methods, achieving improvements of 14.4%, 13.5%, and 11.9% in the Hits@10 metric for the link prediction task, respectively. Furthermore, KG-FIT yields substantial performance gains of 12.6%, 6.7%, and 17.7% compared to the structure-based base models upon which it is built. These results highlight the effectiveness of KG-FIT in incorporating open-world knowledge from LLMs to significantly enhance the expressiveness and informativeness of KG embeddings.