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International Semantic Web Conference

ISWC 2014: The Semantic Web – ISWC 2014 pp 98–113Cite as

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Scientific Lenses to Support Multiple Views over Linked Chemistry Data

Scientific Lenses to Support Multiple Views over Linked Chemistry Data

  • Colin Batchelor24,
  • Christian Y. A. Brenninkmeijer25,
  • Christine Chichester26,
  • Mark Davies27,
  • Daniela Digles28,
  • Ian Dunlop25,
  • Chris T. Evelo29,
  • Anna Gaulton27,
  • Carole Goble25,
  • Alasdair J. G. Gray30,
  • Paul Groth31,
  • Lee Harland32,
  • Karen Karapetyan24,
  • Antonis Loizou31,
  • John P. Overington27,
  • Steve Pettifer25,
  • Jon Steele24,
  • Robert Stevens25,
  • Valery Tkachenko24,
  • Andra Waagmeester29,
  • Antony Williams24 &
  • …
  • Egon L. Willighagen29 
  • Conference paper
  • 3501 Accesses

  • 13 Citations

  • 21 Altmetric

Part of the Lecture Notes in Computer Science book series (LNISA,volume 8796)

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 different lenses which vary the equivalence rules to be applied based on the context and interpretation of the links.

Keywords

  • Link Data
  • Imatinib Mesylate
  • Equivalence Criterion
  • Nucleic Acid Research
  • Ontology Match

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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

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  1. Colin Batchelor
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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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Batchelor, C. et al. (2014). Scientific Lenses to Support Multiple Views over Linked Chemistry Data. In: , 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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