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- data-science-and-symbolic-ai-synergies-challenges-and-opportunities-0 type Preprint assertion.
- data-science-and-symbolic-ai-synergies-challenges-and-opportunities-0 type PositionPaper assertion.
- author-list _1 0000-0001-8149-5890 assertion.
- author-list__1 _2 0000-0003-0169-8159 assertion.
- data-science-and-symbolic-ai-synergies-challenges-and-opportunities-0 term-revisionOf data-science-and-symbolic-ai-synergies-challenges-and-opportunities assertion.
- 0000-0001-8149-5890 name "Robert Hoehndorf" assertion.
- 01q3tbs38 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" assertion.
- 02dxx6824 name "Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, USA" assertion.
- 0000-0003-0169-8159 name "Núria Queralt-Rosinach" assertion.
- data-science-and-symbolic-ai-synergies-challenges-and-opportunities-0 title "Data Science and Symbolic AI: synergies, challenges and opportunities" assertion.
- data-science-and-symbolic-ai-synergies-challenges-and-opportunities-0 date "2017-04-10" assertion.
- data-science-and-symbolic-ai-synergies-challenges-and-opportunities-0 abstract "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." assertion.
- 0000-0001-8149-5890 email "robert.hoehndorf@kaust.edu.sa" assertion.
- 0000-0001-8149-5890 affiliation 01q3tbs38 assertion.
- 0000-0003-0169-8159 affiliation 02dxx6824 assertion.