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}
}