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Triple Feature Propagation (TFP)

This is the code of Triple Feature Propagation (TFP) introduced in our paper: "Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment Decoding".arxiv

Datasets

The dataset and the embedding we processed can be downloaded at GoogleDrive. You can also use the same datasets in DATTI.

  • ent_ids_1: ids for entities in source KG;
  • ent_ids_2: ids for entities in target KG;
  • rel_ids_1: ids for relations in source KG;
  • rel_ids_2: ids for relations in target KG;
  • sup_ent_ids: training entity pairs;
  • ref_ent_ids: testing entity pairs;
  • triples_1: relation triples encoded by ids in source KG;
  • triples_2: relation triples encoded by ids in target KG;

Just run main.py

Environment

  • Python == 3.7.0
  • tensorflow == 2.6.0
  • Numpy
  • tqdm

Acknoledgement

We appreciate DATTI for their open-source contributions.

Cite

Please consider citing this paper if you use the code or data from our work. Thanks a lot ~

@article{wang2024gradient,
  title={Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment Decoding},
  author={Wang, Yuanyi and Sun, Haifeng and Wang, Jingyu and Qi, Qi and Sun, Shaoling and Liao, Jianxin},
  journal={arXiv preprint arXiv:2401.12798},
  year={2024}
}