Scientific Lenses to Support Multiple Views over Linked Chemistry Data

Colin R Batchelor; Christian Y A Brenninkmeijer; Christine Chichester; Mark Davies; Daniela Digles; Ian Dunlop; Chris T A Evelo; Anna Gaulton; Carole A Goble; Alasdair J G Gray; Paul T Groth; Lee Harland; Karen Karapetyan; Antonis Loizou; John P Overington; Steve Pettifer; Jon Steele; Robert Stevens; Valery Tkachenko; Andra Waagmeester; Antony J Williams; Egon L Willighagen

doi:10.1007/978-3-319-11964-9_7

Scientific Lenses to Support Multiple Views over Linked Chemistry Data

Colin R Batchelor, Christian Y A Brenninkmeijer, Christine Chichester, Mark Davies, Daniela Digles, Ian Dunlop, Chris T A Evelo, Anna Gaulton, Carole A Goble, Alasdair J G Gray, Paul T Groth, Lee Harland, Karen Karapetyan, Antonis Loizou, John P Overington, Steve Pettifer, Jon Steele, Robert Stevens, Valery Tkachenko, Andra WaagmeesterAntony J Williams, Egon L Willighagen

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

Original language	English
Title of host publication	The Semantic Web - ISWC 2014 - 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part I
Publisher	Springer Nature
Pages	98-113
Number of pages	16
ISBN (Electronic)	9783319119649
ISBN (Print)	9783319119632
DOIs	https://doi.org/10.1007/978-3-319-11964-9_7
Publication status	Published - 23 Oct 2014

Publication series

Name	ISWC: International Semantic Web Conference
Publisher	Springer Nature
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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Open-PHACTS An open integrated and sustainable chemistry, biology and pharmacology knowledge resource for drug discovery
Goble, C., Pettifer, S., Stevens, R., Gray, A., Soiland-Reyes, S. & Brenninkmeijer, C.
1/03/11 → 28/02/14
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Soiland-Reyes, S., 1 Apr 2021
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Open Access
Scientific Lenses over Linked Data: An approach to support task specific views of the data. A vision.
Brenninkmeijer, C., Evelo, C., Goble, C., Gray, A. JG., Groth, P., Pettifer, S., Stevens, R., Williams, A. J. & Willighagen, E. L., Nov 2012, Proceedings of the 2nd International Workshop on Linked Science 2012 Tackling Big Data (LISC'12): co-located with 11th International Semantic Web Conference (ISWC'12). Kauppinen, T., Pouchard, L. C. & Keßler, C. (eds.). RWTH Aachen University, (CEUR Workshop Proceedings; vol. 951).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Batchelor, C. R., Brenninkmeijer, C. Y. A., Chichester, C., Davies, M., Digles, D., Dunlop, I., Evelo, C. T. A., Gaulton, A., Goble, C. A., Gray, A. J. G., Groth, P. T., Harland, L., Karapetyan, K., Loizou, A., Overington, J. P., Pettifer, S., Steele, J., Stevens, R., Tkachenko, V., ... Willighagen, E. L. (2014). Scientific Lenses to Support Multiple Views over Linked Chemistry Data. In The Semantic Web - ISWC 2014 - 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part I (pp. 98-113). (ISWC: International Semantic Web Conference). Springer Nature. https://doi.org/10.1007/978-3-319-11964-9_7

Batchelor, Colin R ; Brenninkmeijer, Christian Y A ; Chichester, Christine et al. / Scientific Lenses to Support Multiple Views over Linked Chemistry Data. The Semantic Web - ISWC 2014 - 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part I. Springer Nature, 2014. pp. 98-113 (ISWC: International Semantic Web Conference).

@inproceedings{5eb9b5f82f13478aa99d2b4cc3c5cf4c,

title = "Scientific Lenses to Support Multiple Views over Linked Chemistry Data",

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.",

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Batchelor, CR, Brenninkmeijer, CYA, Chichester, C, Davies, M, Digles, D, Dunlop, I, Evelo, CTA, Gaulton, A, Goble, CA, Gray, AJG, Groth, PT, Harland, L, Karapetyan, K, Loizou, A, Overington, JP, Pettifer, S, Steele, J, Stevens, R, Tkachenko, V, Waagmeester, A, Williams, AJ & Willighagen, EL 2014, Scientific Lenses to Support Multiple Views over Linked Chemistry Data. in The Semantic Web - ISWC 2014 - 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part I. ISWC: International Semantic Web Conference, Springer Nature, pp. 98-113. https://doi.org/10.1007/978-3-319-11964-9_7

Scientific Lenses to Support Multiple Views over Linked Chemistry Data. / Batchelor, Colin R; Brenninkmeijer, Christian Y A; Chichester, Christine et al.
The Semantic Web - ISWC 2014 - 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part I. Springer Nature, 2014. p. 98-113 (ISWC: International Semantic Web Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Batchelor, Colin R

AU - Brenninkmeijer, Christian Y A

AU - Chichester, Christine

AU - Davies, Mark

AU - Digles, Daniela

AU - Dunlop, Ian

AU - Evelo, Chris T A

AU - Gaulton, Anna

AU - Goble, Carole A

AU - Gray, Alasdair J G

AU - Groth, Paul T

AU - Harland, Lee

AU - Karapetyan, Karen

AU - Loizou, Antonis

AU - Overington, John P

AU - Pettifer, Steve

AU - Steele, Jon

AU - Stevens, Robert

AU - Tkachenko, Valery

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AU - Williams, Antony J

AU - Willighagen, Egon L

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SN - 9783319119632

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BT - The Semantic Web - ISWC 2014 - 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part I

PB - Springer Nature

ER -

Batchelor CR, Brenninkmeijer CYA, Chichester C, Davies M, Digles D, Dunlop I et al. Scientific Lenses to Support Multiple Views over Linked Chemistry Data. In The Semantic Web - ISWC 2014 - 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part I. Springer Nature. 2014. p. 98-113. (ISWC: International Semantic Web Conference). doi: 10.1007/978-3-319-11964-9_7

Scientific Lenses to Support Multiple Views over Linked Chemistry Data

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Open-PHACTS An open integrated and sustainable chemistry, biology and pharmacology knowledge resource for drug discovery

Research output

Open PHACTS Linksets 2.1.1

Scientific Lenses over Linked Data: An approach to support task specific views of the data. A vision.

Cite this