Representation Learning of Taxonomies for Taxonomy Matching

Abstract

Taxonomy matching aims to discover categories alignments between two taxonomies, which is an important operation of knowledge sharing task to benefit many applications. The existing methods for taxonomy matching mostly depend on string lexical features and domain-specific information. In this paper, we consider the method of representation learning of taxonomies, which projects categories and relationships into low-dimensional vector spaces. We propose a method to takes advantages of category hierarchies and siblings, which exploits a low-dimensional semantic space to modeling categories relations by translating operations in the semantic space. We take advantage of maximum weight matching problem on bipartite graphs to model taxonomy matching problem, which runs in polynomial time to generate optimal categories alignments for two taxonomies in a global manner. Experimental results on OAEI benchmark datasets show that our method significantly outperforms the baseline methods in taxonomy matching.