Enabling OntologyBased Search: A Case Study in the Bioinformatics Domain The most common Internet search engines often fail to provide the most appropriate results according to usersu0027 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, semanticbased 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, ontologyenabled search methodology. The search methodology utilizes stateoftheart ontologies namely, EDAM and SWO, and the Apache Solr freetext annotator, in order to semantically annotate bioinformatics resources maintained by the ELIXIRu0027s 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.

NCBO Resource Index: OntologyBased Search and Mining of Biomedical Resources The volume of publicly available data in biomedicine is constantly increasing. However, this data is stored in different formats on different platforms. Integrating this data will enable us to facilitate the pace of medical discoveries by providing scientists with a unified view of this diverse information. Under the auspices of the National Center for Biomedical Ontology, we have developed the Resource Indexxe2x80x94a growing, largescale index of more than twenty diverse biomedical resources. The resources include heterogeneous data from a variety of repositories maintained by different researchers from around the world. Furthermore, we use a set of 200 publicly available ontologies, also contributed by researchers in various domains, to annotate and to aggregate these descriptions. We use the semantics that the ontologies encode, such as different properties of classes, the class hierarchies, and the mappings between ontologies in order to improve the search experience for the Resource Index user. Our user interface enables scientists to search the multiple resources quickly and efficiently using domain terms, without even being aware that there is semantics under the hood.