Generating Explanations to Understand and Repair Embedding-based Entity Alignment

This repository is the official implementation of ExEA, the method proposed in paper Generating Explanations to Understand and Repair Embedding-based Entity Alignment.

Entity alignment is the task to seek identical entities in different knowledge graphs, which is a long-standing research topic in the database and Semantic Web fields.

The development of deep learning techniques motivates recent work to embed entities in vector space and find entity alignment via the nearest neighbor search.

Although embedding-based entity alignment has gained marked success in recent years, it still remains a “black box” with few explanations for entity alignment decisions.

In this paper, we present the first work that generates human-readable explanations for understanding and repairing embedding-based entity alignment results.

We first compare the neighbor entities and relations of an entity alignment pair to build the subgraph matching graph as a local explanation.

We then construct an alignment dependency graph to understand the given pair from an abstract perspective.

Finally, we repair the entity alignment results by using alignment dependency graphs to resolve three types of alignment conflicts.

Experiments on benchmark datasets demonstrate the effectiveness and generalization ability of the proposed framework in generating explanations and repairing results for embedding-based entity alignment.

Environment

The essential packages and recommened version to run the code:

• python3 (>=3.7)
• pytorch (1.11.0+cu113)
• numpy (1.21.5)
• torch-scatter (2.0.9, see the following)
• scipy (1.7.3)
• tabulate (0.8.9)
• sklearn

Run ExEA

There is a folder for each model, organized as follows:

 - model/     
     |- datasets/   
     |- saved_model/    
     |- src/  

datasets stores the DBP15K dataset and the generated explanation will be placed under the corresponding sub-dataset. The trained model is saved under the saved_model. The code files are placed in src. The implementation of LORE and Anchor comes from the github repositories given in the original papers.

For example, To run Explanation Generation by ExEA on the zh -en dataset , enter src/ and run:

$ python main.py zh EG

To run Entity Alignment Repair by ExEA on the zh -en dataset , enter src/ and run:

$ python main.py zh repair

To run Explanation Generation by baseline, such as lime, on the zh-en dataset , enter src/ and run:

$ python main.py zh lime

Based on the above operations, important features that are sorted will be obtained. Then run the following command to get the selected topk features as explanations.

$ python select_exp.py zh lime --num k