Building a biomedical ontology recommender web service.
J Biomed Semantics. 2010; 1 Suppl 1:S1
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Searching for the correct ontology but not sure if it already exists? This article from the National Center for Biomedical Ontology (NCBO) details a new web application that should help. The tool suggests possible ontologies via an analysis that looks at keywords within a textual corpus submitted by the researcher and the ontologies that already cover available domains in the NCBO. By promoting ontology reuse, this valuable tool should help us avoid the construction of multiple competing ontologies.
Altman R: F1000Prime Recommendation of [Jonquet C et al., J Biomed Semantics 2010, 1 Suppl 1:S1]. In F1000Prime, 01 Feb 2012; DOI: 10.3410/f.13551956.14930056. F1000Prime.com/13551956#eval14930056
F1000Prime Recommendations, Dissents and Comments for [Jonquet C et al., J Biomed Semantics 2010, 1 Suppl 1:S1]. In F1000Prime, 19 Jun 2013; F1000Prime.com/13551956
Nathan Baker and Dennis Thomas 11 Jan 2011
BMC Bioinformatics. 2007; 8 Suppl 3:S2
Charles Auffray 29 May 2007
Science. 2007 Apr 13; 316(5822):222-34
Science. 2008 Dec 19; 322(5909):1857-61
Stephen Lockett 05 Mar 2009
ABSTRACT : BACKGROUND : Researchers in biomedical informatics use ontologies and terminologies to annotate their data in order to facilitate data integration and translational discoveries. As the use of ontologies for annotation of biomedical datasets has risen, a common challenge is to identify ontologies that are best suited to annotating specific datasets. The number and variety of biomedical ontologies is large, and it is cumbersome for a researcher to figure out which ontology to use. METHODS : We present the Biomedical Ontology Recommender web service. The system uses textual metadata or a set of keywords describing a domain of interest and suggests appropriate ontologies for annotating or representing the data. The service makes a decision based on three criteria. The first one is coverage, or the ontologies that provide most terms covering the input text. The second is connectivity, or the ontologies that are most often mapped to by other ontologies. The final criterion is size, or the number of concepts in the ontologies. The service scores the ontologies as a function of scores of the annotations created using the National Center for Biomedical Ontology (NCBO) Annotator web service. We used all the ontologies from the UMLS Metathesaurus and the NCBO BioPortal. RESULTS : We compare and contrast our Recommender by an exhaustive functional comparison to previously published efforts. We evaluate and discuss the results of several recommendation heuristics in the context of three real world use cases. The best recommendations heuristics, rated 'very relevant' by expert evaluators, are the ones based on coverage and connectivity criteria. The Recommender service (alpha version) is available to the community and is embedded into BioPortal.
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