P-NAL: an Effective and Interpretable Entity Alignment Method
Chuanhao Xu Jingwei Cheng Fu Zhang
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. The structural and side information are usually utilized via
embedding propagation, aggregation or interaction. 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
P-NAL, an entity alignment method that captures two types of logical inference
paths with Non-Axiomatic Logic (NAL). Type 1 is the bridge-like inference path
between to-be-aligned entity pairs, consisting of two relation/attribute
triples and a similarity sentence between the other two entities. Type 2 links
the entity pair by their embeddings. P-NAL iteratively aligns entities and
relations by integrating the conclusions of the inference paths. Moreover, our
method is logically interpretable and extensible due to the expressiveness of
NAL. Our proposed method is suitable for various EA settings. Experimental
results show that our method 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. To our knowledge, we present the first in-depth
analysis of entity alignment's basic principles from a unified logical
perspective.