Emerging Practices for Mapping and Linking Life Sciences Data Using RDF - A Case Series
25 Pages Posted: 27 Jun 2018 Publication Status: Accepted
Members of the W3C Health Care and Life Sciences Interest Group (HCLS IG) have published a variety of genomic and drug-related datasets 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 both the workflow and many of the challenges that may be faced when using the workflow to create new Linked Data resources. The first case study describes the creation of linked RDF data from microarray datasets while the second discusses a linked RDF dataset 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 dataset 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 useable by users, Semantic Web 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.
Keywords: Semantic Web, Linked Data, provenance, ontology, health care, life sciences
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