Ontology paper: Emerging practices for mapping and linking life sciences data using RDF - A case series

  • Authors:

  • M. Scott Marshall;Richard Boyce;Helena F. Deus;Jun Zhao;Egon L. Willighagen;Matthias Samwald;Elgar Pichler;Janos Hajagos;Eric Prud'Hommeaux;Susie Stephens

  • Affiliations:

  • Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands and Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands;Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA;Digital Enterprise Research Institute, National University of Ireland at Galway, Ireland;Department of Zoology, University of Oxford, Oxford, UK;Division of Molecular Toxicology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden;Section for Medical Expert and Knowledge-Based Systems, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria and Vienna University of ...;Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA;Stony Brook University School of Medicine, Stony Brook, NY, USA;World Wide Web Consortium, MIT, Cambridge, USA;Janssen Research & Development, LLC, Radnor, PA, USA

  • Venue:
  • Web Semantics: Science, Services and Agents on the World Wide Web
  • Year:
  • 2012

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Abstract

Members of the W3C Health Care and Life Sciences Interest Group (HCLS IG) have published a variety of genomic and drug-related data sets as Resource Description Framework (RDF) triples. This experience has helped the interest group define a general data workflow for mapping health care and life science (HCLS) data to RDF and linking it with other Linked Data sources. This paper presents the workflow along with four case studies that demonstrate the workflow and addresses many of the challenges that may be faced when creating new Linked Data resources. The first case study describes the creation of linked RDF data from microarray data sets while the second discusses a linked RDF data set created from a knowledge base of drug therapies and drug targets. The third case study describes the creation of an RDF index of biomedical concepts present in unstructured clinical reports and how this index was linked to a drug side-effect knowledge base. The final case study describes the initial development of a linked data set from a knowledge base of small molecules. This paper also provides a detailed set of recommended practices for creating and publishing Linked Data sources in the HCLS domain in such a way that they are discoverable and usable by people, software agents, and applications. These practices are based on the cumulative experience of the Linked Open Drug Data (LODD) task force of the HCLS IG. While no single set of recommendations can address all of the heterogeneous information needs that exist within the HCLS domains, practitioners wishing to create Linked Data should find the recommendations useful for identifying the tools, techniques, and practices employed by earlier developers. In addition to clarifying available methods for producing Linked Data, the recommendations for metadata should also make the discovery and consumption of Linked Data easier.