Data Science and Symbolic AI: synergies, challenges and opportunities

Tracking #: 440-1420


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 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.


Supplementary Files (optional): 

Previous Version: 


  • Reviewed

Data repository URLs: 


Date of Submission: 

Monday, April 10, 2017

Date of Decision: 

Thursday, April 27, 2017

Nanopublication URLs:



Solicited Reviews:

1 Comment