VN-Network-and-Further-Refinement

VN Network: Embedding Newly Emerging Entities with Virtual Neighbors

For the CIKM version to reproduce the results of the paper

Run the fb15_main.py and specify dataset and cuda

For the refine version to achieve more accurate result

The command can be found in the run.sh

Many useful tools

• We also provide our data sample tools, the pipeline rule mine and instantiation
• See construct_dataset and rule_mine

Reference

@inproceedings{He Yongquan,
  author    = {Yongquan He and
               Zhihan Wang and
               Peng Zhang and
               Zhaopeng Tu and
               Zhaochun Ren},
  editor    = {Mathieu d'Aquin and
               Stefan Dietze and
               Claudia Hauff and
               Edward Curry and
               Philippe Cudr{\'{e}}{-}Mauroux},
  title     = {{VN} Network: Embedding Newly Emerging Entities with Virtual Neighbors},
  booktitle = {{CIKM} '20: The 29th {ACM} International Conference on Information
               and Knowledge Management, Virtual Event, Ireland, October 19-23, 2020},
  pages     = {505--514},
  publisher = {{ACM}},
  year      = {2020},
  url       = {https://doi.org/10.1145/3340531.3411865},
  doi       = {10.1145/3340531.3411865},
  timestamp = {Sun, 25 Jul 2021 11:54:58 +0200},
  biburl    = {https://dblp.org/rec/conf/cikm/HeWZTR20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{wang2023Iterative,
 author    = {Zihan Wang, Kai Zhao, Yongquan He, Zhumin Chen, Pengjie Ren, Maarten de Rijke, and Zhaochun Ren},
 title     = {Iteratively Learning Representations for Unseen Entities with Inter-Rule Correlations},
 booktitle = {{CIKM} '23: the 32nd ACM International Conference on Information and Knowledge Management, October 21–25, 2023, Birmingham, UK.},
 year      = {2023}
}