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dc.contributor.authorOliveira, Daniela
dc.contributor.authorButt, Anila Sahar
dc.contributor.authorHaller, Armin
dc.contributor.authorRebholz-Schuhmann, Dietrich
dc.contributor.authorSahay, Ratnesh
dc.date.accessioned2018-07-31T10:34:21Z
dc.date.issued2018-03-20
dc.identifier.citationOliveira, Daniela, Butt, Anila Sahar, Haller, Armin, Rebholz-Schuhmann, Dietrich, & Sahay, Ratnesh. (2018). Where to search top-K biomedical ontologies? Briefings in Bioinformatics, bby015-bby015. doi: 10.1093/bib/bby015en_IE
dc.identifier.issn1477-4054
dc.identifier.urihttp://hdl.handle.net/10379/7444
dc.description.abstractMotivation Searching for precise terms and terminological definitions in the biomedical data space is problematic, as researchers find overlapping, closely related and even equivalent concepts in a single or multiple ontologies. Search engines that retrieve ontological resources often suggest an extensive list of search results for a given input term, which leads to the tedious task of selecting the best-fit ontological resource (class or property) for the input term and reduces user confidence in the retrieval engines. A systematic evaluation of these search engines is necessary to understand their strengths and weaknesses in different search requirements. Result We have implemented seven comparable Information Retrieval ranking algorithms to search through ontologies and compared them against four search engines for ontologies. Free-text queries have been performed, the outcomes have been judged by experts and the ranking algorithms and search engines have been evaluated against the expert-based ground truth (GT). In addition, we propose a probabilistic GT that is developed automatically to provide deeper insights and confidence to the expert-based GT as well as evaluating a broader range of search queries. Conclusion The main outcome of this work is the identification of key search factors for biomedical ontologies together with search requirements and a set of recommendations that will help biomedical experts and ontology engineers to select the best-suited retrieval mechanism in their search scenarios. We expect that this evaluation will allow researchers and practitioners to apply the current search techniques more reliably and that it will help them to select the right solution for their daily work. Availability The source code (of seven ranking algorithms), ground truths and experimental results are available at https://github.com/danielapoliveira/bioont-search-benchmarken_IE
dc.description.sponsorshipThis work has been supported by the Science Foundation Ireland (grant number SFI/12/RC/2289).en_IE
dc.formatapplication/pdfen_IE
dc.language.isoenen_IE
dc.publisherOxford University Pressen_IE
dc.relation.ispartofBriefings in Bioinformaticsen
dc.subjectInformation retrievalen_IE
dc.subjectRanking algorithmsen_IE
dc.subjectOntologyen_IE
dc.subjectHealthcare and life sciencesen_IE
dc.subjectSemantic Weben_IE
dc.subjectLinked dataen_IE
dc.titleWhere to search top-K biomedical ontologies?en_IE
dc.typeArticleen_IE
dc.date.updated2018-07-06T15:40:38Z
dc.identifier.doi10.1093/bib/bby015
dc.local.publishedsourcehttps://doi.org/10.1093/bib/bby015en_IE
dc.description.peer-reviewedpeer-reviewed
dc.contributor.funderScience Foundation Irelanden_IE
dc.description.embargo2019-03-20
dc.internal.rssid14544072
dc.local.contactRatnesh Nandan Sahay, Deri, Dangan Business Park, Nui Galway. 5253 Email: ratnesh.sahay@nuigalway.ie
dc.local.copyrightcheckedYes
dc.local.versionACCEPTED
dcterms.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/
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