ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs

Related tags

Deep Learning QE-ConE
Overview

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

  1. Download the datasets here.
  2. Move the zipped datasets to the root directory of ConE and run unzip -d data KG_data.zip.
  3. 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]

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Comments
  • About entity name

    About entity name

    Nice work, thanks for sharing the code. By the way, I wonder where can I find the names of all entities in the dataset? Currently, I can only get the entity indices w/o semantic meanings.

    Thanks

    opened by Cranooooooo 2
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MIRA Lab
Laboratory of Machine Intelligence Research and Applications at University of Science and Technology of China
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