RANM

Source code and datasets for TKDE2022 paper: [Semi-supervised Entity Alignment via Relation-based Adaptive Neighborhood Matching]

Datasets

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

Initial datasets WN31-15K is from OpenEA. Initial datasets DBP-15K is from JAPE. Initial datasets DWY100K is from BootEA.

Take the dataset EN_DE(V1) as an example, the folder "pre4" contains:

• kg1_ent_dict: ids for entities in source KG;
• kg2_ent_dict: ids for entities in target KG;
• 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 attribute value feature matrix initialized by word vectors;

Environment

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

Running

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

python3 align_exc.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, Lina Wei, and Yuncheng Jiang*. Semi-supervised Entity Alignment via Relation-based Adaptive Neighborhood Matching, IEEE Transactions on Knowledge and Data Engineering(TKDE), Early Access Article. DOI: 10.1109/TKDE.2022.3222811, 2022. (CCF A)