A Re-evaluation of Knowledge Graph Completion Methods

Source code for ACL 2020 paper on Knowledge Graph Evaluation

Overview

Effect of different evaluation protocols on recent KG embedding methods on FB15k-237 dataset. For TOP and BOTTOM, we report changes in performance with respect to RANDOM protocol. Please refer to paper for more details.

Dependencies

• Compatible with TensorFlow 1.x, PyTorch 1.x, and Python 3.x.
• Dependencies can be installed using requirements.txt.

Usage:

• Codes for different models are included in their respective directories.
• Run proproc.sh for unziping the data.

Citation:

Please cite the following paper if you use this code in your work.

@ARTICLE{kgeval,
       author = {{Sun}, Zhiqing and {Vashishth}, Shikhar and {Sanyal}, Soumya and
         {Talukdar}, Partha and {Yang}, Yiming},
        title = "{A Re-evaluation of Knowledge Graph Completion Methods}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Computation and Language},
         year = "2019",
        month = "Nov",
          eid = {arXiv:1911.03903},
        pages = {arXiv:1911.03903},
archivePrefix = {arXiv},
       eprint = {1911.03903},
 primaryClass = {cs.CL},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2019arXiv191103903S},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

For any clarification, comments, or suggestions please create an issue or contact Zhiqing or Shikhar.