RPR-RHGT

Source code and datasets for IJCAI 2022 paper: [Entity Alignment with Reliable Path Reasoning and Relation-aware Heterogeneous Graph Transformer]

Datasets

Please first download the main datasets here , path datasets here and extract them into datasets/ directory.

Initial datasets WN31-15K and DBP-15K are from OpenEA and JAPE.

Initial datasets DWY100K is from BootEA.

Take the dataset EN_DE(V1) as an example, the folder "pre " of main datasets contains:

• kg1_ent_dict: ids for entities in source KG;
• kg2_ent_dict: ids for entities in target KG;
• ref_ent_ids: entity links encoded by ids;
• rel_triples_id: relation triples encoded by ids;
• kgs_num: statistics of the number of entities, relations, attributes, and attribute values;
• entity_embedding.out: the input entity name feature matrix initialized by word vectors;

The folder "pre " of path datasets contains:

• path_neigh_dict: Path and its associated head and tail entities;
• rpath_sort_dict: Paths and their frequency numbers;

Environment

• Python>=3.7
• pytorch>=1.7.0
• tensorboardX>=2.1.0
• Numpy
• json

Running

To run RPR-RHGT model on WN31-15K and DBP-15K, use the following script:

python3 align/exc_plan.py

To run RPR-RHGT model DWY100K, use the following script:

python3 align100K/exc_plan100K.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 difficulty or question in running code and reproducing expriment results, please email to cwswork@qq.com.

Citation

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

Weishan Cai, Wenjun Ma, Jieyu Zhan, and Yuncheng Jiang, “Entity Alignment with Reliable Path Reasoning and Relation-aware Heterogeneous Graph Transformer”. In [Proceedings of the 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI-ECAI 2022)],3:1930-1937.(https://www.ijcai.org/proceedings/2022/268)