Predicting the Extension of Biomedical Ontologies

Developing and extending a biomedical ontology is a very demanding task that can never be considered complete given our ever-evolving understanding of the life sciences. Extension in particular can benefit from the automation of some of its steps, thus releasing experts to focus on harder tasks. Here we present a strategy to support the automation of change capturing within ontology extension where the need for new concepts or relations is identified. Our strategy is based on predicting areas of an ontology that will undergo extension in a future version by applying supervised learning over features of previous ontology versions. We used the Gene Ontology as our test bed and obtained encouraging results with average f-measure reaching 0.79 for a subset of biological process terms. Our strategy was also able to outperform state of the art change capturing methods. In addition we have identified several issues concerning prediction of ontology evolution, and have delineated a general framework for ontology extension prediction. Our strategy can be applied to any biomedical ontology with versioning, to help focus either manual or semi-automated extension methods on areas of the ontology that need extension. Author Summary: Biomedical knowledge is complex and in constant evolution and growth, making it difficult for researchers to keep up with novel discoveries. Ontologies have become essential to help with this issue since they provide a standardized format to describe knowledge that facilitates its storing, sharing and computational analysis. However, the effort to keep a biomedical ontology up-to-date is a demanding and costly task involving several experts. Much of this effort is dedicated to the addition of new elements to extend the ontology to cover new areas of knowledge. We have developed an automated methodology to identify areas of the ontology that need extension based on past versions of the ontology as well as external data such as references in scientific literature and ontology usage. This can be a valuable help to semi-automated ontology extension systems, since they can focus on the subdomains of the identified ontology areas thus reducing the amount of information to process, which in turn releases ontology developers to focus on more complex ontology evolution tasks. By contributing to a faster rate of ontology evolution, we hope to positively impact ontology-based applications such as natural language processing, computer reasoning, information integration or semantic querying of heterogenous data.