  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.
