Data Science with Semantic Technologies: Application to Information Systems Development

ABSTRACT

Various semantic technologies such as ontologies, machine learning, or artificial intelligence-based are being used today with data science for the purpose of explaining the meaning of data, and making this explanation exploitable by computer processing. Although some quick and brief reports do exist, to the best of our knowledge, the literature lacks a detailed study reporting why, when and how semantic technologies are used with data science. This paper is a theoretical review aiming at providing an insight into data science with semantic technologies. We characterize this research topic through a framework called DS2T helping to understand data science with semantic technologies and giving a comprehensive overview of the field through different, but complementary views. The proposed framework may be used to position research studies integrating semantic technologies with data science, compare them, understand new trends, and identify opportunities and open issues related to a given application domain. Software development processes are used as illustration domain.

KEYWORDS:

• Data science
• semantic technologies
• semantics
• classification framework
• software development processes

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

^a https://www.gartner.com/en/documents/4008669

^b The result is available at https://cutt.ly/VGgPHZs