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dc.contributor.authorHoehndorf, Robert
dc.contributor.authorQueralt-Rosinach, Núria
dc.date.accessioned2017-06-08T09:42:43Z
dc.date.available2017-06-08T09:42:43Z
dc.date.issued2017-06-02
dc.identifier.citationRobert Hoehndorf, Núria Queralt-Rosinach. Data science and symbolic AI: Synergies, challenges and opportunities. Tobias Kuhn, editor. DS. IOS Press; 2017; 1–12. doi:10.3233/DS-170004
dc.identifier.issn2451-8492
dc.identifier.issn2451-8484
dc.identifier.doi10.3233/ds-170004
dc.identifier.urihttp://hdl.handle.net/10754/624879
dc.description.abstractSymbolic approaches to artificial intelligence represent things within a domain of knowledge through physical symbols, combine symbols into symbol expressions, and manipulate symbols and symbol expressions through inference processes. While a large part of Data Science relies on statistics and applies statistical approaches to artificial intelligence, there is an increasing potential for successfully applying symbolic approaches as well. Symbolic representations and symbolic inference are close to human cognitive representations and therefore comprehensible and interpretable; they are widely used to represent data and metadata, and their specific semantic content must be taken into account for analysis of such information; and human communication largely relies on symbols, making symbolic representations a crucial part in the analysis of natural language. Here we discuss the role symbolic representations and inference can play in Data Science, highlight the research challenges from the perspective of the data scientist, and argue that symbolic methods should become a crucial component of the data scientists’ toolbox.
dc.publisherIOS Press
dc.relation.urlhttp://content.iospress.com/articles/data-science/ds004
dc.rightsThis article is published online with Open Access and distributed under the terms of the Creative Commons Attribution License (CC BY 4.0).
dc.subjectSymbolic AI, machine learning, statistics, empirical science
dc.titleData science and symbolic AI: Synergies, challenges and opportunities
dc.typeArticle
dc.contributor.departmentBio-Ontology Research Group (BORG)
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalData Science
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, USA.
kaust.personHoehndorf, Robert
refterms.dateFOA2018-06-13T15:04:24Z


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