Preprint: Data Science and Symbolic AI: synergies, challenges and opportunities

Full identifier: https://w3id.org/np/RA9oo9RIbNIT1l_5fgHBKvnGhfmLbc0t1wyOWl5EVuGEY

 Preprint: Data Science and Symbolic AI: synergies, challenges and opportunities PositionPaper Preprint ▼ comment approve/disapprove edit as derived nanopublication create new with same template

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https://datasciencehub.net/pap...challenges-and-opportunities-0 data-science-and-symbolic-ai-synergies-challenges-and-opportunities-0 http://www.w3.org/1999/02/22-rdf-syntax-ns#type is a http://purl.org/spar/fabio/PositionPaper position paper .

https://datasciencehub.net/pap...challenges-and-opportunities-0 data-science-and-symbolic-ai-synergies-challenges-and-opportunities-0 http://purl.org/dc/terms/title has the title (this is a literal) "Data Science and Symbolic AI: synergies, challenges and opportunities" .

https://datasciencehub.net/pap...challenges-and-opportunities-0 data-science-and-symbolic-ai-synergies-challenges-and-opportunities-0 http://purl.org/dc/terms/abstract has the abstract (this is a literal) "Symbolic approaches to artificial intelligence represent things within a domain of knowledge through physical symbols, combine symbols into symbol ex- pressions, and manipulate symbols and symbol expressionsNN through inference processes. 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." .

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https://orcid.org/0000-0001-8149-5890 Robert Hoehndorf http://xmlns.com/foaf/0.1/name has the name (this is a literal) "Robert Hoehndorf" .

https://orcid.org/0000-0002-1267-0234 Tobias Kuhn http://xmlns.com/foaf/0.1/name has the name (this is a literal) "Tobias Kuhn" .

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Robert Hoehndorf et al. (authors), Tobias Kuhn (creator), 26 May 2025, 10:11:16 UTC

References