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- DS-170004 type PositionPaper assertion.
- DS-170004 type Preprint assertion.
- author-list _1 0000-0001-8149-5890 assertion.
- author-list__1 _2 0000-0003-0169-8159 assertion.
- DS-170004 title "Data Science and Symbolic AI: synergies, challenges and opportunities" assertion.
- 0000-0003-0169-8159 name "Núria Queralt-Rosinach" 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.
- DS-170004 abstract "Symbolic approaches to artificial intelligence represent things within a domain of knowledge through physical symbols, combine symbols into symbol expressions and structures, and manipulate symbols and symbol expressions and structures through inference processes. While a large part of Data Science relies on statistics and applies statistical approaches to artificial intelligence, there is an increasing potential for successfully applying symbolic approaches as well. Sym- bolic representations and symbolic inference are close to human cognitive repre- sentations 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 natu- ral 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." assertion.
- DS-170004 date "2017-02-21" assertion.
- 0000-0001-8149-5890 email "robert.hoehndorf@kaust.edu.sa" assertion.
- 0000-0003-0169-8159 affiliation 02dxx6824 assertion.
- 0000-0001-8149-5890 affiliation 01q3tbs38 assertion.