NCBO Resource Index: Ontology-based search and mining of biomedical resources

  • Authors:

  • Clement Jonquet;Paea Lependu;Sean Falconer;Adrien Coulet;Natalya F. Noy;Mark A. Musen;Nigam H. Shah

  • Affiliations:

  • Stanford Center for Biomedical Informatics Research, Stanford University, 251 Campus Drive, Stanford, CA 94305-5479, USA and Laboratory of Informatics, Robotics, and Microelectronics of Montpellie ...;Stanford Center for Biomedical Informatics Research, Stanford University, 251 Campus Drive, Stanford, CA 94305-5479, USA;Stanford Center for Biomedical Informatics Research, Stanford University, 251 Campus Drive, Stanford, CA 94305-5479, USA;Stanford Center for Biomedical Informatics Research, Stanford University, 251 Campus Drive, Stanford, CA 94305-5479, USA and Lorraine Informatics Research and Applications Laboratory (LORIA), INRI ...;Stanford Center for Biomedical Informatics Research, Stanford University, 251 Campus Drive, Stanford, CA 94305-5479, USA;Stanford Center for Biomedical Informatics Research, Stanford University, 251 Campus Drive, Stanford, CA 94305-5479, USA;Stanford Center for Biomedical Informatics Research, Stanford University, 251 Campus Drive, Stanford, CA 94305-5479, USA

  • Venue:
  • Web Semantics: Science, Services and Agents on the World Wide Web
  • Year:
  • 2011

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Abstract

The volume of publicly available data in biomedicine is constantly increasing. However, these data are stored in different formats and on different platforms. Integrating these 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 (NCBO), we have developed the Resource Index - a growing, large-scale ontology-based index of more than twenty heterogeneous biomedical resources. The resources come from a variety of repositories maintained by organizations from around the world. We use a set of over 200 publicly available ontologies contributed by researchers in various domains to annotate the elements in these resources. 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.''