Data science and symbolic AI: Synergies, challenges and opportunities
Name:
ds-prepress2Fds--1--1-ds0042Fds--1-ds004.pdf
Size:
265.5Kb
Format:
PDF
Description:
uncorrected proof
Type
ArticleKAUST Department
Bio-Ontology Research Group (BORG)Computational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2017-06-02Permanent link to this record
http://hdl.handle.net/10754/624879
Metadata
Show full item recordAbstract
Symbolic approaches to artificial intelligence 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 artificial intelligence, 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.Citation
Robert Hoehndorf, Núria Queralt-Rosinach. Data science and symbolic AI: Synergies, challenges and opportunities. Tobias Kuhn, editor. DS. IOS Press; 2017; 1–12. doi:10.3233/DS-170004Publisher
IOS PressJournal
Data ScienceAdditional Links
http://content.iospress.com/articles/data-science/ds004ae974a485f413a2113503eed53cd6c53
10.3233/ds-170004