R. Hoehndorf, N. Queralt-Rosinach
Data Science, pp. 1-12, (2017)
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.