Semantic Data Discovery from Social Big Data

Bilal Abu-Salih (), Pornpit Wongthongtham (), Dengya Zhu (), Kit Yan Chan () and Amit Rudra ()
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Bilal Abu-Salih: The University of Jordan
Pornpit Wongthongtham: The University of Western Australia
Dengya Zhu: Curtin University
Kit Yan Chan: Curtin University
Amit Rudra: Curtin University

Chapter Chapter 4 in Social Big Data Analytics, 2021, pp 89-112 from Springer

Abstract: Abstract Due to the large volume of data and information generated by a multitude of social data sources, it is a huge challenge to manage and extract useful knowledge, especially given the different forms of data, streaming data and uncertainty and ambiguity of data. Hence, there are still challenges in this area of BD analytics research to capture, store, process, visualise, query, and manipulate datasets to derive meaningful information that is specific to an application’s domain. This chapter attempts to address this problem by studying Semantic Analytics and domain knowledge modelling, and to what extent these technologies can be utilised toward better understanding to the social textual contents. In particular, the chapter gives an overview of semantic analysis and domain ontology followed by shedding light on domain knowledge modelling, inference, semantic storage, and publicly available semantic tools and APIs. Also, the theoretical notion of Knowledge Graphs is reported and their interlinking with SBD is discussed. The utility of the semantic analytics is demonstrated and evaluated through a case study on social data in the context of politics domain.

Keywords: Semantic analytics; Semantic modelling; Sematic inference and interlinking; Knowledge graphs; Twitter mining (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/978-981-33-6652-7_4

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