Data Science and Symbolic AI: synergies, challenges and opportunities

Tracking #: 440-1420

Authors:

NameORCID
Robert HoehndorfORCID logo https://orcid.org/0000-0001-8149-5890
Núria Queralt-RosinachORCID logo https://orcid.org/0000-0003-0169-8159


Responsible editor: 

Tobias Kuhn

Submission Type: 

Position Paper

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.

Manuscript: 

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Tags: 

  • Reviewed

Data repository URLs: 

none

Date of Submission: 

Monday, April 10, 2017

Date of Decision: 

Thursday, April 27, 2017

Decision: 

Accept

Solicited Reviews:


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