https://w3id.org/kpxl/ios/ds/np/RAmJB5YSNX8Vd6O_nUh7C6pDDBkIM0-gEvLBoSQg3sEdQ/Head https://w3id.org/kpxl/ios/ds/np/RAmJB5YSNX8Vd6O_nUh7C6pDDBkIM0-gEvLBoSQg3sEdQ http://www.nanopub.org/nschema#hasAssertion https://w3id.org/kpxl/ios/ds/np/RAmJB5YSNX8Vd6O_nUh7C6pDDBkIM0-gEvLBoSQg3sEdQ/assertion https://w3id.org/kpxl/ios/ds/np/RAmJB5YSNX8Vd6O_nUh7C6pDDBkIM0-gEvLBoSQg3sEdQ http://www.nanopub.org/nschema#hasProvenance https://w3id.org/kpxl/ios/ds/np/RAmJB5YSNX8Vd6O_nUh7C6pDDBkIM0-gEvLBoSQg3sEdQ/provenance https://w3id.org/kpxl/ios/ds/np/RAmJB5YSNX8Vd6O_nUh7C6pDDBkIM0-gEvLBoSQg3sEdQ http://www.nanopub.org/nschema#hasPublicationInfo https://w3id.org/kpxl/ios/ds/np/RAmJB5YSNX8Vd6O_nUh7C6pDDBkIM0-gEvLBoSQg3sEdQ/pubinfo https://w3id.org/kpxl/ios/ds/np/RAmJB5YSNX8Vd6O_nUh7C6pDDBkIM0-gEvLBoSQg3sEdQ http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.nanopub.org/nschema#Nanopublication https://w3id.org/kpxl/ios/ds/np/RAmJB5YSNX8Vd6O_nUh7C6pDDBkIM0-gEvLBoSQg3sEdQ/assertion http://id.crossref.org/issn/2451-8492 http://purl.org/dc/terms/title Data Science https://doi.org/10.3233/DS-170004 http://purl.org/dc/terms/abstract Symbolic approaches to Artificial Intelligence (AI) 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 AI, 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. https://doi.org/10.3233/DS-170004 http://purl.org/dc/terms/date 2017-10-17 https://doi.org/10.3233/DS-170004 http://purl.org/dc/terms/isPartOf http://id.crossref.org/issn/2451-8492 https://doi.org/10.3233/DS-170004 http://purl.org/dc/terms/title Data Science and symbolic AI: Synergies, challenges and opportunities https://doi.org/10.3233/DS-170004 http://purl.org/ontology/bibo/issue 1-2 https://doi.org/10.3233/DS-170004 http://purl.org/ontology/bibo/volume 1 https://doi.org/10.3233/DS-170004 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/PositionPaper https://orcid.org/0000-0001-8149-5890 http://schema.org/affiliation https://ror.org/01q3tbs38 https://orcid.org/0000-0001-8149-5890 http://schema.org/email robert.hoehndorf@kaust.edu.sa https://orcid.org/0000-0001-8149-5890 http://xmlns.com/foaf/0.1/name Robert Hoehndorf https://orcid.org/0000-0003-0169-8159 http://schema.org/affiliation https://ror.org/02dxx6824 https://orcid.org/0000-0003-0169-8159 http://xmlns.com/foaf/0.1/name Núria Queralt-Rosinach https://ror.org/01q3tbs38 http://xmlns.com/foaf/0.1/name Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia. Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia https://ror.org/02dxx6824 http://xmlns.com/foaf/0.1/name Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, USA https://w3id.org/kpxl/ios/ds/np/RAmJB5YSNX8Vd6O_nUh7C6pDDBkIM0-gEvLBoSQg3sEdQ/author-list http://www.w3.org/1999/02/22-rdf-syntax-ns#_1 https://orcid.org/0000-0001-8149-5890 https://w3id.org/kpxl/ios/ds/np/RAmJB5YSNX8Vd6O_nUh7C6pDDBkIM0-gEvLBoSQg3sEdQ/author-list__1 http://www.w3.org/1999/02/22-rdf-syntax-ns#_2 https://orcid.org/0000-0003-0169-8159 https://w3id.org/kpxl/ios/ds/np/RAmJB5YSNX8Vd6O_nUh7C6pDDBkIM0-gEvLBoSQg3sEdQ/provenance https://w3id.org/kpxl/ios/ds/np/RAmJB5YSNX8Vd6O_nUh7C6pDDBkIM0-gEvLBoSQg3sEdQ/assertion http://www.w3.org/ns/prov#wasAttributedTo https://orcid.org/0000-0001-8149-5890 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