Semantic data discovery from Social Big Data

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.

💡 Research Summary

This paper addresses the challenge of managing and extracting useful knowledge from the vast amount of data and information generated by various social data sources. The complexity arises due to different forms of data, streaming data, and the uncertainty and ambiguity inherent in such datasets. The authors focus on Semantic Analytics and domain knowledge modeling as key technologies for better understanding social textual content.

The paper provides an overview of semantic analysis and domain ontology, followed by a discussion on domain knowledge modeling, inference, semantic storage, and publicly available semantic tools and APIs. It also delves into the theoretical concept of Knowledge Graphs and their interlinking with Social Big Data (SBD). The utility of semantic analytics is demonstrated through a case study in the context of the political domain.

The authors highlight that while there are significant challenges in capturing, storing, processing, visualizing, querying, and manipulating datasets to derive meaningful information specific to an application’s domain, Semantic Analytics offers promising solutions. By leveraging these technologies, researchers can better manage the complexity of social data and extract valuable insights relevant to their field of study.

The case study presented showcases how semantic analytics can be applied in practice, providing a practical demonstration of its effectiveness in extracting useful knowledge from complex datasets. This paper serves as an important resource for researchers and practitioners interested in advancing BD analytics research, particularly within the context of social data analysis.