Abstract
BackgroundResearchers 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.MethodsWe 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.ResultsWe 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.
Highlights
Biomedical ontologies are widely used to design information retrieval systems, to facilitate interoperability between data repositories, and to develop systems that parse, annotate or index biomedical data resources
Biomedical ontologies have been identified as a crucial means for representing knowledge and annotate biomedical data in order to create a biomedical semantic web
We have discussed the need of ontology recommendation in order to design new ontologies and annotate data
Summary
Background Biomedical ontologies are widely used to design information retrieval systems, to facilitate interoperability between data repositories, and to develop systems that parse, annotate or index biomedical data resources. Biomedical researchers use ontologies and terminologies to annotate (or tag) their data with ontology terms for better data integration and translational discoveries [1,2]. The number and variety (formats, locations) of biomedical ontologies is so large that choosing one for an annotation task or for designing a specific application is a difficult challenge. The process to choose a set of ontologies to use is oftentimes a hard, manual and time consuming task for researchers. Researchers in biomedical informatics use ontologies and terminologies to annotate their data in order to facilitate data integration and translational discoveries. The number and variety of biomedical ontologies is large, and it is cumbersome for a researcher to figure out which ontology to use
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