A Web-Based Framework for Classification and Integration of News Articles Big Data using Ontology

Abstract

Around 2.5 quintillion bytes of data have been created online: out of which most of the data has been generated in the last two years. To generate this huge amount of data from different sources, many devices are being utilized such as sensors to get the data about climate information, social networking sites, banking records, e-commerce records, etc. This data is known as Big Data. It mainly consists of three 3v’s volumes, velocity, and variety. Variety of data discusses about different formats of data originating from various data foundations. Hence, the big data variety’s issue is significant in explaining some genuine challenges. The semantic Web is utilized as an Integrator to join information from different sorts of data foundations like web services, social databases, and spreadsheets and so on and in various formats. The semantic Web is an all-encompassing type of the present web that gives simpler methods to look, reuse, join and offer the data. In this manner, it is along these lines seen as a combiner transversely over different things, information applications, and systems. This paper is an effort to uncover the nature of big data and a brief survey on the use of various semantic web-based methods and tools to add value to today’s big data. In addition, it discusses a case study on performing various machine learning functionalities on news articles and proposes a web-based framework for classification and integration of news articles big data using ontologies.