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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-3138
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dc.contributor.authorSima, Ana-Claudia-
dc.contributor.authorStockinger, Kurt-
dc.contributor.authorde Farias, Tarcisio Mendes-
dc.contributor.authorGil, Manuel-
dc.date.accessioned2019-07-25T07:24:05Z-
dc.date.available2019-07-25T07:24:05Z-
dc.date.issued2019-
dc.identifier.isbn978-1-4939-9073-3de_CH
dc.identifier.isbn978-1-4939-9074-0de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/17721-
dc.description.abstractBiological databases are growing at an exponential rate, currently being among the major producers of Big Data, almost on par with commercial generators, such as YouTube or Twitter. While traditionally biological databases evolved as independent silos, each purposely built by a different research group in order to answer specific research questions; more recently significant efforts have been made toward integrating these heterogeneous sources into unified data access systems or interoperable systems using the FAIR principles of data sharing. Semantic Web technologies have been key enablers in this process, opening the path for new insights into the unified data, which were not visible at the level of each independent database. In this chapter, we first provide an introduction into two of the most used database models for biological data: relational databases and RDF stores. Next, we discuss ontology-based data integration, which serves to unify and enrich heterogeneous data sources. We present an extensive timeline of milestones in data integration based on Semantic Web technologies in the field of life sciences. Finally, we discuss some of the remaining challenges in making ontology-based data access (OBDA) systems easily accessible to a larger audience. In particular, we introduce natural language search interfaces, which alleviate the need for database users to be familiar with technical query languages. We illustrate the main theoretical concepts of data integration through concrete examples, using two well-known biological databases: a gene expression database, Bgee, and an orthology database, OMA.de_CH
dc.language.isoende_CH
dc.publisherSpringerde_CH
dc.relation.ispartofEvolutionary genomics : statistical and computational methodsde_CH
dc.relation.ispartofseriesMethods in Molecular Biologyde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectData integrationde_CH
dc.subjectKeyword searchde_CH
dc.subjectKnowledge representationde_CH
dc.subjectOntology-based data accessde_CH
dc.subjectQuery processingde_CH
dc.subjectRDF storede_CH
dc.subjectRelational databasede_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.titleSemantic integration and enrichment of heterogeneous biological databasesde_CH
dc.typeBuchbeitragde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
zhaw.publisher.placeNew Yorkde_CH
dc.identifier.doi10.1007/978-1-4939-9074-0_22de_CH
dc.identifier.doi10.21256/zhaw-3138-
dc.identifier.pmid31278681de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end690de_CH
zhaw.pages.start655de_CH
zhaw.parentwork.editorAnisimova, Maria-
zhaw.publication.statuspublishedVersionde_CH
zhaw.series.number1910de_CH
zhaw.publication.reviewEditorial reviewde_CH
zhaw.funding.snf167149de_CH
zhaw.webfeedBiomedical String Analysisde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedInformation Engineeringde_CH
zhaw.funding.zhawBio-SODA: Enabling Complex, Semantic Queries to Bioinformatics Databases through Intuitive Searching over Datade_CH
zhaw.author.additionalNode_CH
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Sima, A.-C., Stockinger, K., de Farias, T. M., & Gil, M. (2019). Semantic integration and enrichment of heterogeneous biological databases. In M. Anisimova (Ed.), Evolutionary genomics : statistical and computational methods (pp. 655–690). Springer. https://doi.org/10.1007/978-1-4939-9074-0_22
Sima, A.-C. et al. (2019) ‘Semantic integration and enrichment of heterogeneous biological databases’, in M. Anisimova (ed.) Evolutionary genomics : statistical and computational methods. New York: Springer, pp. 655–690. Available at: https://doi.org/10.1007/978-1-4939-9074-0_22.
A.-C. Sima, K. Stockinger, T. M. de Farias, and M. Gil, “Semantic integration and enrichment of heterogeneous biological databases,” in Evolutionary genomics : statistical and computational methods, M. Anisimova, Ed. New York: Springer, 2019, pp. 655–690. doi: 10.1007/978-1-4939-9074-0_22.
SIMA, Ana-Claudia, Kurt STOCKINGER, Tarcisio Mendes DE FARIAS und Manuel GIL, 2019. Semantic integration and enrichment of heterogeneous biological databases. In: Maria ANISIMOVA (Hrsg.), Evolutionary genomics : statistical and computational methods. New York: Springer. S. 655–690. ISBN 978-1-4939-9073-3
Sima, Ana-Claudia, Kurt Stockinger, Tarcisio Mendes de Farias, and Manuel Gil. 2019. “Semantic Integration and Enrichment of Heterogeneous Biological Databases.” In Evolutionary Genomics : Statistical and Computational Methods, edited by Maria Anisimova, 655–90. New York: Springer. https://doi.org/10.1007/978-1-4939-9074-0_22.
Sima, Ana-Claudia, et al. “Semantic Integration and Enrichment of Heterogeneous Biological Databases.” Evolutionary Genomics : Statistical and Computational Methods, edited by Maria Anisimova, Springer, 2019, pp. 655–90, https://doi.org/10.1007/978-1-4939-9074-0_22.


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