RDGCN

Source code and datasets for IJCAI 2019 paper: Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs.

Initial datasets are from GCN-Align and JAPE.

Dependencies

• Python>=3.5
• Tensorflow>=1.8.0
• Scipy>=1.1.0
• 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).

Datasets

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

There are three cross-lingual datasets in this folder:

• fr-en
• ja-en
• zh-en

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;
• 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;

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:

Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Rui Yan, Dongyan Zhao. Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19, pages 5278-5284, 2019.

@inproceedings{ijcai2019-733,
  title={Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs},
  author={Wu, Yuting and Liu, Xiao and Feng, Yansong and Wang, Zheng and Yan, Rui and Zhao, Dongyan},
  booktitle={Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, {IJCAI-19}},            
  pages={5278--5284},
  year={2019},
}