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doi:10.3233/DS-170004

Full identifier: https://doi.org/10.3233/DS-170004

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http://purl.org/spar/fabio/PositionPaper PositionPaper ( )

References

Nanopublication	Part	Subject	Predicate	Object	Published By	Published On
https://w3id.org/kpxl/ios/ds/np/RAmJB5YSNX... RAmJB5YSNX	links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion	doi:10.3233/DS-170004	http://www.w3.org/1999/02/22-rdf-syntax-ns#type type	http://purl.org/spar/fabio/PositionPaper PositionPaper	Tobias Kuhn	2025-05-05T09:13:17.554Z
https://w3id.org/kpxl/ios/ds/np/RAmJB5YSNX... RAmJB5YSNX	links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion	doi:10.3233/DS-170004	http://purl.org/dc/terms/isPartOf isPartOf	http://id.crossref.org/issn/2451-8492 2451-8492	Tobias Kuhn	2025-05-05T09:13:17.554Z
https://w3id.org/kpxl/ios/ds/np/RAmJB5YSNX... RAmJB5YSNX	links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion	doi:10.3233/DS-170004	http://purl.org/dc/terms/title title	Data Science and symbolic AI: Synergies, challenges and opportunities	Tobias Kuhn	2025-05-05T09:13:17.554Z
https://w3id.org/kpxl/ios/ds/np/RAmJB5YSNX... RAmJB5YSNX	links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion	doi:10.3233/DS-170004	http://purl.org/dc/terms/date date	2017-10-17	Tobias Kuhn	2025-05-05T09:13:17.554Z
https://w3id.org/kpxl/ios/ds/np/RAmJB5YSNX... RAmJB5YSNX	links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion	doi:10.3233/DS-170004	http://purl.org/dc/terms/abstract 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.	Tobias Kuhn	2025-05-05T09:13:17.554Z
https://w3id.org/kpxl/ios/ds/np/RAmJB5YSNX... RAmJB5YSNX	links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion	doi:10.3233/DS-170004	http://purl.org/ontology/bibo/issue issue	1-2	Tobias Kuhn	2025-05-05T09:13:17.554Z
https://w3id.org/kpxl/ios/ds/np/RAmJB5YSNX... RAmJB5YSNX	links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion	doi:10.3233/DS-170004	http://purl.org/ontology/bibo/volume volume	1	Tobias Kuhn	2025-05-05T09:13:17.554Z
https://w3id.org/kpxl/ios/ds/np/RAmJB5YSNX... RAmJB5YSNX	links a nanopublication to its pubinfo http://www.nanopub.org/nschema#hasPublicationInfo pubinfo	https://w3id.org/kpxl/ios/ds/np/RAmJB5YSNX... RAmJB5YSNX	http://purl.org/nanopub/x/introduces introduces	doi:10.3233/DS-170004	Tobias Kuhn	2025-05-05T09:13:17.554Z