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
Learning Objective: After participating in this session, the learner should be better able to:
- Understand the importance of ontology alignment in the biomedical domain
- Understand how neural networks can be trained to encode biomedical knowledge from ontologies into knowledge embeddings and how such knowledge embeddings can be used for ontology alignment.
- Understand the effect of the Generative Adversarial Network training paradigm and bootstrapping for ontology alignment
Ramon Maldonado (Presenter)
The University of Texas at Dallas
Sanda Harabagiu, The University of Texas at Dallas