IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-981-33-6652-7_4.html
   My bibliography  Save this book chapter

Semantic Data Discovery from Social Big Data

In: Social Big Data Analytics

Author

Listed:
  • 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)

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.

Suggested Citation

  • Bilal Abu-Salih & Pornpit Wongthongtham & Dengya Zhu & Kit Yan Chan & Amit Rudra, 2021. "Semantic Data Discovery from Social Big Data," Springer Books, in: Social Big Data Analytics, chapter 0, pages 89-112, Springer.
    • Handle: RePEc:spr:sprchp:978-981-33-6652-7_4
    DOI: 10.1007/978-981-33-6652-7_4
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-981-33-6652-7_4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.