Enabling Ontology-Based Search: A Case Study in the Bioinformatics Domain

The most common Internet search engines often fail to provide the most appropriate results according to users' quests and needs. Research in the field aims towards alternative ways to search and filter the flooded and chaotic Internet content. In this context, semantic search techniques have emerged. Over the years, semantic-based search engines have gained popularity, mainly because of their efficiency, but with limited penetration to the wide public, even if many attempts have already delivered encouraging results. In this paper we present a semantic, ontology-enabled search methodology. The search methodology utilizes state-of-the-art ontologies namely, EDAM and SWO, and the Apache Solr free-text annotator, in order to semantically annotate bioinformatics resources maintained by the ELIXIR's bio.tools bioinformatics resources registry. The semantically annotated descriptions of the tools are utilized in order to map query terms into respective ontological terms. The introduced search methodology is tested on a set of indicative queries from SEQanswers bioinformatics discussion forum. The results indicate that the whole approach is well performing, achieving adequate success hit rates, and quite efficient computational response times.