Ontology mapping for semantically enabled applications.

Journal: Drug discovery today
Published Date:

Abstract

In this review, we provide a summary of recent progress in ontology mapping (OM) at a crucial time when biomedical research is under a deluge of an increasing amount and variety of data. This is particularly important for realising the full potential of semantically enabled or enriched applications and for meaningful insights, such as drug discovery, using machine-learning technologies. We discuss challenges and solutions for better ontology mappings, as well as how to select ontologies before their application. In addition, we describe tools and algorithms for ontology mapping, including evaluation of tool capability and quality of mappings. Finally, we outline the requirements for an ontology mapping service (OMS) and the progress being made towards implementation of such sustainable services.

Authors

  • Ian Harrow
    Pistoia Alliance Ontologies Mapping Project, Pistoia Alliance Inc, USA. ian.harrow@pistoiaalliance.org.
  • Rama Balakrishnan
    Genentech Inc., South San Francisco, CA, USA.
  • Ernesto Jiménez-Ruiz
    Department of Informatics, University of Oslo, Oslo, Norway.
  • Simon Jupp
    European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Trust Genome Campus, Hinxton Cambridge, CB10 1SD UK.
  • Jane Lomax
    ‡ Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus Hinxton, Cambridge, CB10 1SA, UK.
  • Jane Reed
    Linguamatics Ltd, Cambridge, UK.
  • Martin Romacker
    Roche Pharma Research and Early Development, pRED Informatics, Roche Innovation Center, Basel, Switzerland.
  • Christian Senger
    OSTHUS GmbH, Aachen, Germany.
  • Andrea Splendiani
    Novartis, Basel, Switzerland.
  • Jabe Wilson
    Elsevier RELX, London, UK.
  • Peter Woollard
    GlaxoSmithKline R&D, Stevenage, UK.