Ontology-Aware Biomedical Relation Extraction

Abstract

Automatically extracting relationships from biomedical texts among multiple sorts of entities is an essential task in biomedical natural language processing with numerous applications, such as drug development or repurposing, precision medicine, and other biomedical tasks requiring knowledge discovery. Current Relation Extraction systems mostly use one set of features, either as text, or more recently, as graph structures. The state-of-the-art systems often use resource-intensive hence slow algorithms and largely work for a particular type of relationship. However, a simple yet agile system that learns from different sets of features has the advantage of adaptability over different relationship types without an extra burden required for system re-design.

We model RE as a classification task and propose a new multi-channel deep neural network designed to process textual and graph structures in separate input channels. We extend a Recurrent Neural Network with a Convolutional Neural Network to process three sets of features, namely, tokens, types, and graphs. We demonstrate that entity type and ontology graph structure provide better representations than simple token-based representations for Relation Extraction. We also experiment with various sources of knowledge, including data resources in the Unified Medical Language System to test our hypothesis. Extensive experiments on four well-studied biomedical benchmarks with different relationship types show that our system outperforms earlier ones. Thus, our system has state-of-the-art performance and allows processing millions of full-text scientific articles in a few days on one typical machine.