HGCN-JE-JR

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

HGCN-JE-JR 최신버젼 다운로드

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# HGCN-JE-JR

Source code and datasets for EMNLP 2019 paper: ***[Jointly Learning Entity and Relation Representations for Entity Alignment](https://arxiv.org/pdf/1909.09317.pdf)***.

## Datasets

> Please first download the datasets [here](https://drive.google.com/open?id=1mfaeLXdqFnOHLYBXiTHWI7MLwtfTgPYQ) and extract them into `data/` directory.

Initial datasets are from [GCN-Align](https://github.com/1049451037/GCN-Align) and [JAPE](https://github.com/nju-websoft/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 wyting@pku.edu.cn.

## 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",
}
```

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