/WFRE

Wasserstein-Fisher-Rao Embedding: Logical Query Embeddings with Local Comparison and Global Transport (Findings-ACL 2023)

Primary LanguagePython

Wasseretein-Fisher-Rao Embedding

The implementation for ACL 2023 findings paper:

Wasserstein-Fisher-Rao Embedding: Logical Query Embeddings with Local Comparison and Global Transport

by Zihao Wang, Weizhi Fei, Hang Yin, Yangqiu Song, Ginny Y. Wong, and Simon See.

Prepare the data

The KG data (FB15k, FB15k-237, NELL995) should be put into under 'data/' folder. We use the data provided in the KGReasoning. The structure of the data folder should be at least and we follow the query type of EFO-1 and transfer the data.

data
	|---FB15k-237-betae
	|---FB15k-betae
	|---NELL-betae

The OpsTree is generated by binary_formula_iterator in fol/foq_v2.py. The overall process is managed in formula_generation.py. Transform beta queries to EFO-1 is the next step.

To generate the formula and transform the queries data, just run

python formula_generation.py
python transform_beta_data.py

Run experiments on different knowledge graphs

The hyperparameters in three datasets is provided in config/papers. To get our results in paper just run

python main.py -config config/papers/NELL.yaml