This repository contains the code for the paper "KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge" (NeurIPS 2024). Paper Link
To enable precompute, you need to put a file named "openai_api.key" (with your OpenAI API key in there) under code/precompute, then run the following command with a specified dataset (FB16K-237 in this case):
cd code/precompute
python cluster.py --dataset FB15K-237 --output_dir ../../processed_data # precomputation for seed hierarchy
cd llm_refine
python llm_refine.py --dataset FB15K-237 --model gpt-4o-2024-05-13 # LLM-Guided Hierarchy Refinement (LHR)
cd ..
python cluster.py --dataset FB15K-237 --output_dir ../../processed_data # precomputation for llm hierarchywhere the first call of cluster.py is used to build seed hierarchy; llm_refine.py is used to refine the seed hierarchy with LLM; The second call of cluster.py is used to build the final hierarchy with LLM.
bash runs_rotate.sh
bash runs_tucker.sh| File | KG-FIT with KGE base models | Text and Hierarchical Constraints | Text Embedding within Entity Embedding |
|---|---|---|---|
model_common.py |
All models except TuckER and ConvE | On negative batches | Frozen |
model_flex.py |
All models except TuckER and ConvE | On negative batches | On Fire |
model_p_anc.py |
All models except TuckER and ConvE | On both positive and negative batches | Frozen |
model_tucker_conve.py |
KG-FIT-TuckER and KG-FIT-ConvE | On both positive and negative batches | Frozen |
@article{jiang2024kg,
title={Kg-fit: Knowledge graph fine-tuning upon open-world knowledge},
author={Jiang, Pengcheng and Cao, Lang and Xiao, Cao Danica and Bhatia, Parminder and Sun, Jimeng and Han, Jiawei},
journal={Advances in Neural Information Processing Systems},
volume={37},
pages={136220--136258},
year={2024}
}Thank you for your interest in our work!