Scientific lenses to support multiple views over linked chemistry data

Colin Batchelor, Christian Y A Brenninkmeijer, Christine Chichester, Mark Davies, Daniela Digles, Ian Dunlop, Chris T Evelo, Anna Gaulton, Carole Goble, Alasdair Gray, Paul Groth, Lee Harland, Karen Karapetyan, Antonis Loizou, John Overington, Steve Pettifer, jon steele, Robert Stevens, Valery Tkachenko, Andra Waagmeester & 2 others Antony J Williams, Egon Willighagen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationThe Semantic Web - ISWC 2014
Subtitle of host publication13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part I
Pages98-113
DOIs
Publication statusPublished - 2014

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Batchelor, C., Brenninkmeijer, C. Y. A., Chichester, C., Davies, M., Digles, D., Dunlop, I., ... Willighagen, E. (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) https://doi.org/10.1007/978-3-319-11964-9_7
Batchelor, Colin ; Brenninkmeijer, Christian Y A ; Chichester, Christine ; Davies, Mark ; Digles, Daniela ; Dunlop, Ian ; Evelo, Chris T ; Gaulton, Anna ; Goble, Carole ; Gray, Alasdair ; Groth, Paul ; Harland, Lee ; Karapetyan, Karen ; Loizou, Antonis ; Overington, John ; Pettifer, Steve ; steele, jon ; Stevens, Robert ; Tkachenko, Valery ; Waagmeester, Andra ; Williams, Antony J ; Willighagen, Egon. / 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. 2014. pp. 98-113
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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.",
author = "Colin Batchelor and Brenninkmeijer, {Christian Y A} and Christine Chichester and Mark Davies and Daniela Digles and Ian Dunlop and Evelo, {Chris T} and Anna Gaulton and Carole Goble and Alasdair Gray and Paul Groth and Lee Harland and Karen Karapetyan and Antonis Loizou and John Overington and Steve Pettifer and jon steele and Robert Stevens and Valery Tkachenko and Andra Waagmeester and Williams, {Antony J} and Egon Willighagen",
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Batchelor, C, Brenninkmeijer, CYA, Chichester, C, Davies, M, Digles, D, Dunlop, I, Evelo, CT, Gaulton, A, Goble, C, Gray, A, Groth, P, Harland, L, Karapetyan, K, Loizou, A, Overington, J, Pettifer, S, steele, J, Stevens, R, Tkachenko, V, Waagmeester, A, Williams, AJ & Willighagen, E 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. https://doi.org/10.1007/978-3-319-11964-9_7

Scientific lenses to support multiple views over linked chemistry data. / Batchelor, Colin; Brenninkmeijer, Christian Y A; Chichester, Christine; Davies, Mark; Digles, Daniela; Dunlop, Ian; Evelo, Chris T; Gaulton, Anna; Goble, Carole; Gray, Alasdair; Groth, Paul; Harland, Lee; Karapetyan, Karen; Loizou, Antonis; Overington, John; Pettifer, Steve; steele, jon; Stevens, Robert; Tkachenko, Valery; Waagmeester, Andra; Williams, Antony J; Willighagen, Egon.

The Semantic Web - ISWC 2014: 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part I. 2014. p. 98-113.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Evelo, Chris T

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AU - Karapetyan, Karen

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AU - Overington, John

AU - Pettifer, Steve

AU - steele, jon

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AB - 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 C, 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. 2014. p. 98-113 https://doi.org/10.1007/978-3-319-11964-9_7