Data Science and Symbolic AI: synergies, challenges and opportunities

Tracking #: 427-1407


Robert HoehndorfORCID logo
Núria Queralt RosinachORCID logo

Responsible editor: 

Tobias Kuhn

Submission Type: 

Position Paper


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.



  • Reviewed

Data repository URLs: 


Date of Submission: 

Tuesday, February 21, 2017

Date of Decision: 

Friday, March 17, 2017



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