ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs
This is the code of paper ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs. Zhanqiu Zhang, Jie Wang, Jiajun Chen, Shuiwang Ji, Feng Wu. NeurIPS 2021. [arXiv]
Requirements
- Python 3.7
- PyTorch 1.7
- tqdm
Reproduce the Results
- Download the datasets here.
- Move the zipped datasets to the root directory of ConE and run
unzip -d data KG_data.zip
. - Run the scripts in
scripts.sh
.
Citation
If you find this code useful, please consider citing the following paper.
@inproceedings{NEURIPS2021_QECONE,
author = {Zhang, Zhanqiu and Wang, Jie and Jiajun, Chen and Shuiwang, Ji and Feng, Wu},
booktitle = {Advances in Neural Information Processing Systems},
title = {ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs},
year = {2021}
}
Acknowledgement
We refer to the code of KGReasoning. Thanks for their contributions.
Other Repositories
If you are interested in our work, you may find the following papers useful.
Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion. Zhanqiu Zhang, Jianyu Cai, Jie Wang. NeurIPS 2020. [paper] [code]
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction. Zhanqiu Zhang, Jianyu Cai, Yongdong Zhang, Jie Wang. AAAI 2020. [paper] [code]