Bootstrapping Adversarial Learning of Biomedical Ontology Alignments

Abstract

Learning how to automatically align biomedical ontologies has been a long-standing goal, given their ever-growing content and the many applications that rely on them. Because the knowledge graphs underlying biomedical ontologies enable neural learning techniques to acquire knowledge embeddings as representations of these ontologies, neural learning can also consider ontology alignments. In this paper, we present the Knowledge-graph Alignment & Embedding Generative Adversarial Network (KAEGAN) which learns (a) to represent the relational knowledge from two distinct biomedical ontologies in the form of knowledge embeddings and (b) to use them for ontology alignment, by also relying on the ontology semantics. KAEGAN is a Generative Adversarial Network trained using bootstrapping to iteratively improve the learned alignments. Experimental results show promise, demonstrating that jointly learning ontology alignment and knowledge representation improves upon learning either in isolation.