Generating Explanations to Understand and Repair Embedding-based Entity Alignment
This addresses the interpretability problem for researchers and practitioners using entity alignment in knowledge graphs, though it is incremental as it builds on existing embedding methods.
The paper tackles the lack of explanations in embedding-based entity alignment by introducing a framework that generates local and abstract explanations and repairs alignment conflicts, demonstrating effectiveness across various datasets.
Entity alignment (EA) seeks identical entities in different knowledge graphs, which is a long-standing task in the database research. Recent work leverages deep learning to embed entities in vector space and align them via nearest neighbor search. Although embedding-based EA has gained marked success in recent years, it lacks explanations for alignment decisions. In this paper, we present the first framework that can generate explanations for understanding and repairing embedding-based EA results. Given an EA pair produced by an embedding model, we first compare its neighbor entities and relations to build a matching subgraph as a local explanation. We then construct an alignment dependency graph to understand the pair from an abstract perspective. Finally, we repair the pair by resolving three types of alignment conflicts based on dependency graphs. Experiments on a variety of EA datasets demonstrate the effectiveness, generalization, and robustness of our framework in explaining and repairing embedding-based EA results.