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    Data science and symbolic AI: Synergies, challenges and opportunities

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    ds-prepress2Fds--1--1-ds0042Fds--1-ds004.pdf
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    Type
    Article
    Authors
    Hoehndorf, Robert cc
    Queralt-Rosinach, Núria cc
    KAUST Department
    Bio-Ontology Research Group (BORG)
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2017-06-02
    Permanent link to this record
    http://hdl.handle.net/10754/624879
    
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    Abstract
    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-170004
    Publisher
    IOS Press
    Journal
    Data Science
    DOI
    10.3233/ds-170004
    Additional Links
    http://content.iospress.com/articles/data-science/ds004
    ae974a485f413a2113503eed53cd6c53
    10.3233/ds-170004
    Scopus Count
    Collections
    Articles; Bio-Ontology Research Group (BORG); Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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