albertpenny/hgcn-je-jr

Source code and datasets for EMNLP 2019 paper: Jointly Learning Entity and Relation Representations for Entity Alignment.

HGCN-JE-JR

Source code and datasets for EMNLP 2019 paper: Jointly Learning Entity and Relation Representations for Entity Alignment.

Datasets

Please first download the datasets here and extract them into data/ directory.

Initial datasets are from GCN-Align and JAPE. We manually aligned more relations from the three datasets and removed the ambiguously aligned relation pairs to construct the test sets for relation alignment.

Take the dataset DBP15K (ZH-EN) as an example, the folder "zh_en" contains:

• ent_ids_1: ids for entities in source KG (ZH);
• ent_ids_2: ids for entities in target KG (EN);
• ref_ent_ids: entity links encoded by ids;
• ref_r_ids: relation links encoded by ids;
• rel_ids_1: ids for entities in source KG (ZH);
• rel_ids_2: ids for entities in target KG (EN);
• triples_1: relation triples encoded by ids in source KG (ZH);
• triples_2: relation triples encoded by ids in target KG (EN);
• zh_vectorList.json: the input entity feature matrix initialized by word vectors;

Environment

• Python>=3.5
• Tensorflow>=1.8.0
• Scipy
• Numpy

Due to the limited graphics memory of GPU, we ran our codes using CPUs (40 Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz).

Running

• Modify language or some other settings in include/Config.py
• cd to the directory of main.py
• run main.py

Due to the instability of embedding-based methods, it is acceptable that the results fluctuate a little bit (±1%) when running code repeatedly.

If you have any questions about reproduction, please feel free to email to [email protected].

Citation

If you use this model or code, please cite it as follows:

@inproceedings{wu2019jointly,
    title = "Jointly Learning Entity and Relation Representations for Entity Alignment",
    author = "Wu, Yuting  and
      Liu, Xiao  and
      Feng, Yansong  and
      Wang, Zheng  and
      Zhao, Dongyan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1023",
    doi = "10.18653/v1/D19-1023",
    pages = "240--249",
}