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This article is part of the supplement: Semantic Web Applications and Tools for Life Sciences, 2008

Open Access Highly Accessed Research

A journey to Semantic Web query federation in the life sciences

Kei-Hoi Cheung1*, H Robert Frost2, M Scott Marshall3, Eric Prud'hommeaux4, Matthias Samwald56, Jun Zhao7 and Adrian Paschke8

Author Affiliations

1 Center for Medical Informatics, Yale University School of Medicine, New Haven, CT 06511, USA

2 VectorC, LLC, Hanover, NH 03755, USA

3 Informatics Institute, University of Amsterdam, The Netherlands

4 World Wide Web Consortium, Massachusetts Institute of Technology, Massachusetts, MA 02139, USA

5 Digital Enterprise Research Institute, National University of Ireland Galway, IDA Business Park, Lower Dangan, Galway, Ireland

6 Konrad Lorenz Institute for Evolution and Cognition Research, Altenberg, Austria

7 Department of Zoology, University of Oxford, Oxford, OX1 3PS, UK

8 Freie Universit├Ąt Berlin, Germany

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BMC Bioinformatics 2009, 10(Suppl 10):S10  doi:10.1186/1471-2105-10-S10-S10

Published: 1 October 2009

Abstract

Background

As interest in adopting the Semantic Web in the biomedical domain continues to grow, Semantic Web technology has been evolving and maturing. A variety of technological approaches including triplestore technologies, SPARQL endpoints, Linked Data, and Vocabulary of Interlinked Datasets have emerged in recent years. In addition to the data warehouse construction, these technological approaches can be used to support dynamic query federation. As a community effort, the BioRDF task force, within the Semantic Web for Health Care and Life Sciences Interest Group, is exploring how these emerging approaches can be utilized to execute distributed queries across different neuroscience data sources.

Methods and results

We have created two health care and life science knowledge bases. We have explored a variety of Semantic Web approaches to describe, map, and dynamically query multiple datasets. We have demonstrated several federation approaches that integrate diverse types of information about neurons and receptors that play an important role in basic, clinical, and translational neuroscience research. Particularly, we have created a prototype receptor explorer which uses OWL mappings to provide an integrated list of receptors and executes individual queries against different SPARQL endpoints. We have also employed the AIDA Toolkit, which is directed at groups of knowledge workers who cooperatively search, annotate, interpret, and enrich large collections of heterogeneous documents from diverse locations. We have explored a tool called "FeDeRate", which enables a global SPARQL query to be decomposed into subqueries against the remote databases offering either SPARQL or SQL query interfaces. Finally, we have explored how to use the vocabulary of interlinked Datasets (voiD) to create metadata for describing datasets exposed as Linked Data URIs or SPARQL endpoints.

Conclusion

We have demonstrated the use of a set of novel and state-of-the-art Semantic Web technologies in support of a neuroscience query federation scenario. We have identified both the strengths and weaknesses of these technologies. While Semantic Web offers a global data model including the use of Uniform Resource Identifiers (URI's), the proliferation of semantically-equivalent URI's hinders large scale data integration. Our work helps direct research and tool development, which will be of benefit to this community.