Paper for 2024

This is the code of PipEA introduced in our paper, which is based on PEEA encoder.

The dataset can be unzipped by data.zip in your root.

• 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 environment is provided in requirements.txt.

We also provide faster large-scale strategy for 100K and larger datasets. If you want to apply PipEA on large-scale KG, you can modify and run test.py

You can also apply 15K datasets based on the test.py, which only needs to generate embeddings firstly. The test.py will faster than original code.

Notably, the application on the large-scale KG need tf 2.x, which is different from original environment.

Our large scale environment is:

• fbpca
• tensorflow == 2.4.1
• Python == 3.6.5