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Scientific Lenses to Support Multiple Views over Linked Chemistry Data

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Abstract

When are two entries about a small molecule in different datasets the same? If they have the same drug name, chemical structure, or some other criteria? The choice depends upon the application to which the data will be put. However, existing Linked Data approaches provide a single global view over the data with no way of varying the notion of equivalence to be applied.

In this paper, we present an approach to enable applications to choose the equivalence criteria to apply between datasets. Thus, supporting multiple dynamic views over the Linked Data. For chemical data, we show that multiple sets of links can be automatically generated according to different equivalence criteria and published with semantic descriptions capturing their context and interpretation. This approach has been applied within a large scale public-private data integration platform for drug discovery. To cater for different use cases, the platform allows the application of differentlenses which vary the equivalence rules to be applied based on the context and interpretation of the links.

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Author information

Authors and Affiliations

  1. Royal Society of Chemistry, UK

    Colin Batchelor, Karen Karapetyan, Jon Steele, Valery Tkachenko & Antony Williams

  2. School of Computer Science, University of Manchester, Manchester, UK

    Christian Y. A. Brenninkmeijer, Ian Dunlop, Carole Goble, Steve Pettifer & Robert Stevens

  3. Swiss Institute for Bioinformatics, Switzerland

    Christine Chichester

  4. European Molecular Biology Laboratory European Bioinformatics Institute, Hinxton, UK

    Mark Davies, Anna Gaulton & John P. Overington

  5. Department of Pharmaceutical Chemistry, University of Vienna, Vienna, Austria

    Daniela Digles

  6. Maastricht University, Maastricht, The Netherlands

    Chris T. Evelo, Andra Waagmeester & Egon L. Willighagen

  7. Heriot-Watt University, Edinburgh, UK

    Alasdair J. G. Gray

  8. VU University of Amsterdam, The Netherlands

    Paul Groth & Antonis Loizou

  9. Connected Discovery, UK

    Lee Harland

Authors
  1. Colin Batchelor

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  2. Christian Y. A. Brenninkmeijer

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  3. Christine Chichester

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  4. Mark Davies

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  5. Daniela Digles

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  6. Ian Dunlop

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  7. Chris T. Evelo

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  8. Anna Gaulton

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  9. Carole Goble

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  10. Alasdair J. G. Gray

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  11. Paul Groth

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  12. Lee Harland

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  13. Karen Karapetyan

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  14. Antonis Loizou

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  15. John P. Overington

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  16. Steve Pettifer

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  17. Jon Steele

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  18. Robert Stevens

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  19. Valery Tkachenko

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  20. Andra Waagmeester

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  21. Antony Williams

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  22. Egon L. Willighagen

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Editor information

Editors and Affiliations

  1. Yahoo Labs, Diagonal 177, 08018, Barcelona, Spain

    Peter Mika

  2. Stanford University, 1265 Welch Road, 94305, Stanford, CA, USA

    Tania Tudorache

  3. University of Zurich, DDIS, Zurich, Switzerland

    Abraham Bernstein

  4. IBM Research, Yorktown Heights, NY, USA

    Chris Welty

  5. Information Sciences Institute and Department of Computer Science, University of Southern California, Los Angeles, CA, USA

    Craig Knoblock

  6. Google, USA

    Denny Vrandečić  & Natasha Noy  & 

  7. VU University Amsterdam, The Netherlands

    Paul Groth

  8. University of California, Santa Barbara, CA, USA

    Krzysztof Janowicz

  9. School of Computer Science, The University of Manchester, Manchester, UK

    Carole Goble

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© 2014 Springer International Publishing Switzerland

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Cite this paper

Batchelor, C.et al. (2014). Scientific Lenses to Support Multiple Views over Linked Chemistry Data. In: Mika, P.,et al. The Semantic Web – ISWC 2014. ISWC 2014. Lecture Notes in Computer Science, vol 8796. Springer, Cham. https://doi.org/10.1007/978-3-319-11964-9_7

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