NALA: an Effective and Interpretable Entity Alignment Method
Abstract: Entity alignment (EA) aims to find equivalent entities between two Knowledge Graphs.
Existing embedding-based EA methods usually encode entities as embeddings, triples as embeddings' constraint and learn to align the embeddings.
However, the details of the underlying logical inference steps among the alignment process are usually omitted,
resulting in inadequate inference process.
In this paper, we introduce NALA, an entity alignment method that captures three types of logical inference paths with Non-Axiomatic Logic (NAL).
Type 1&2 align the entity pairs
and type 3 aligns relations.
NALA iteratively aligns entities and relations by integrating the conclusions of the inference paths.
Our method is logically interpretable and extensible by introducing NAL, and thus suitable for various EA settings.
Experimental results show that NALA outperforms state-of-the-art methods in terms of Hits@1, achieving 0.98+ on all three datasets of DBP15K with both supervised and unsupervised settings.
We offer a pioneering in-depth analysis of the fundamental principles of entity alignment, approaching the subject from a unified and logical perspective.
Our code is available at https://anonymous.4open.science/r/NALA-976B.
Paper Type: Long
Research Area: NLP Applications
Research Area Keywords: knowledge graphs; entity alignment; interpretability
Contribution Types: Model analysis & interpretability, NLP engineering experiment
Languages Studied: English; Chinese; Japanese; French
Submission Number: 2690
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