KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge
<|reference_start|>KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge: 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.<|reference_end|>
@article{jiang2024kg-fit:,
    title={KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge},
    author={Pengcheng Jiang, Lang Cao, Cao Xiao, Parminder Bhatia, Jimeng Sun,
  Jiawei Han},
    journal={arXiv preprint arXiv:2405.16412},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.16412},
    primaryClass={cs.CL cs.LG}
}