The code of DATTI: "An Effective and Efficient Entity Alignment Decoding Algorithm via Third-Order Tensor Isomorphism" https://aclanthology.org/2022.acl-long.405/

Datasets

• ent_ids_1: ids for entities in source KG;

• ent_ids_2: ids for entities in target KG;

• rel_ids_1: ids for relations in source KG;

• rel_ids_2: ids for relations in target KG;

• sup_ent_ids: training entity pairs;

• ref_ent_ids: testing entity pairs;

• triples_1: relation triples encoded by ids in source KG;

• triples_2: relation triples encoded by ids in target KG;

• The datasets and pre-trained embeddings could be downloaded from https://github.com/MaoXinn/DATTI/releases

Environment

• Jupyter notebook
• tensorly
• tensorflow == 2.4.1
• Python == 3.6.5
• Numba
• Scipy
• Numpy
• tqdm

Just run Tensor_decoder.ipynb block by block.