Extending ontologies by finding siblings using set expansion techniques

Motivation: Ontologies are an everyday tool in biomedicine to capture and represent knowledge. However, many ontologies lack a high degree of coverage in their domain and need to improve their overall quality and maturity. Automatically extending sets of existing terms will enable ontology engineers to systematically improve text-based ontologies level by level. Results: We developed an approach to extend ontologies by discovering new terms which are in a sibling relationship to existing terms of an ontology. For this purpose, we combined two approaches which retrieve new terms from the web. The first approach extracts siblings by exploiting the structure of HTML documents, whereas the second approach uses text mining techniques to extract siblings from unstructured text. Our evaluation against MeSH (Medical Subject Headings) shows that our method for sibling discovery is able to suggest first-class ontology terms and can be used as an initial step towards assessing the completeness of ontologies. The evaluation yields a recall of 80% at a precision of 61% where the two independent approaches are complementing each other. For MeSH in particular, we show that it can be considered complete in its medical focus area. We integrated the work into DOG4DAG, an ontology generation plugin for the editors OBO-Edit and Protégé, making it the first plugin that supports sibling discovery on-the-fly. Availability: Sibling discovery for ontology is available as part of DOG4DAG (www.biotec.tu-dresden.de/research/schroeder/dog4dag) for both Protégé 4.1 and OBO-Edit 2.1. Contact: ms@biotec.tu-dresden.de; goetz.fabian@biotec.tu-dresden.de Supplementary information: Supplementary data are available at Bioinformatics online.