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While a large part of Data Science relies on statistics and applies statisti- cal approaches to artificial intelligence, there is an increasing potential for success- fully applying symbolic approaches as well. Symbolic representations and sym- bolic inference are close to human cognitive representations and therefore compre- hensible 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 in- formation; 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, high- light 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." } ], "http://purl.org/dc/terms/date" : [ { "@value" : "2017-04-10" } ], "http://purl.org/dc/terms/title" : [ { "@value" : "Data Science and Symbolic AI: synergies, challenges and opportunities" } ], "https://vocab.org/frbr/core#term-revisionOf" : [ { "@id" : "https://datasciencehub.net/paper/data-science-and-symbolic-ai-synergies-challenges-and-opportunities" } ] }, { "@id" : "https://orcid.org/0000-0001-8149-5890", "http://schema.org/affiliation" : [ { "@id" : "https://ror.org/01q3tbs38" } ], "http://schema.org/email" : [ { "@value" : "robert.hoehndorf@kaust.edu.sa" } ], "http://xmlns.com/foaf/0.1/name" : [ { "@value" : "Robert Hoehndorf" } ] }, { "@id" : "https://orcid.org/0000-0003-0169-8159", "http://schema.org/affiliation" : [ { "@id" : "https://ror.org/02dxx6824" } ], "http://xmlns.com/foaf/0.1/name" : [ { "@value" : "Núria Queralt-Rosinach" } ] }, { "@id" : "https://ror.org/01q3tbs38", "http://xmlns.com/foaf/0.1/name" : [ { "@value" : "Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia. 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