Data Science with Semantic Technologies

Abstract

Today, with the development of digital technology, the volume of data on the Internet is increasing at an unprecedented rate. This huge mass of data, which provides many advantages if properly analyzed and managed, is vital for many systems. However, the healthy interpretation and correct management of this data, which is increasing day by day, has become complicated for many systems. One of the biggest reasons underlying this complexity is that most of the data produced in daily life are obtained from different devices and sources such as sensors, phones, tablets, and computers. It poses a fundamentally new set of research challenges, including structuring, sharing, and managing heterogeneous data from different sources in a common framework. Ontologies, which are the heart of semantic technologies, can be used to provide more advanced access, standardize, add extra explanations, and enrich the meaning of heterogeneous data on the Internet. Recent research in this field has focused on creating common metadata using ontologies and on the common representation and management of this data created in daily life. In this study, it will be discussed how semantic technologies and ontologies can be integrated into data science. The extent to which semantic web technologies and ontologies will contribute to data science will be demonstrated. In addition, potential difficulties to be encountered during and after the integration of semantic web technologies and ontologies into data science will be shared. © 2023 CRC Press.