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.

中文翻译：

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来自社交大数据的语义数据发现

由于大量社会数据源生成大量的数据和信息，因此，管理和提取有用的知识面临着巨大的挑战，尤其是考虑到不同形式的数据，流数据以及数据的不确定性和歧义性。因此，在BD分析研究的这一领域中仍然存在挑战，以捕获，存储，处理，可视化，查询和操纵数据集以导出特定于应用程序领域的有意义的信息。本章试图通过研究语义分析和领域知识建模来解决此问题，并在多大程度上可以利用这些技术来更好地理解社会文本内容。特别是，本章概述了语义分析和领域本体，然后重点介绍了领域知识建模，推理，语义存储以及公开可用的语义工具和API。此外，报告了知识图的理论概念，并讨论了它们与SBD的相互联系。通过在政治领域中对社会数据进行案例研究，来证明和评估语义分析的效用。