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Retrieving Information from Social Media using Ontology

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Listed:
  • Tengku Adil Tengku Izhar
  • Mohd Shamsul Mohd Shoid
  • Abdul Ismail Mohd Jawi
  • Trieu Minh Nhut Le

Abstract

People have access to more data in single day than most people that have access to data in the previous decade. This data is created in many forms and it highlights the development of Big Data. Big Data in organizations have transformed the way organizations across industries implement new approach to handle huge amount of data. Organizations rely to this data to achieve specific business priorities. The challenge is how to retrieve this data to be considered relevant for the specific organization activities because determining relevant data is a key to deliver information from massive amounts of data. The aim of this paper is to integrate organizational data and social data using an ontology to retrieve relevant information for efficient decision-making. We investigate how external data such as social media can support internal data such as organizational data in relation to the organizational goals. The results from the case study demonstrate how we incorporate social data and organizational data. This paper demonstrates that ontology provide a platform to integrate social data and organizational data.

Suggested Citation

  • Tengku Adil Tengku Izhar & Mohd Shamsul Mohd Shoid & Abdul Ismail Mohd Jawi & Trieu Minh Nhut Le, 2016. "Retrieving Information from Social Media using Ontology," International Journal of Academic Research in Business and Social Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Business and Social Sciences, vol. 6(9), pages 1-18, September.
    • Handle: RePEc:hur:ijarbs:v:6:y:2016:i:9:p:1-18
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    References listed on IDEAS

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    1. Mauricio Barcellos Almeida & Ricardo Rodrigues Barbosa, 2009. "Ontologies in knowledge management support: A case study," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(10), pages 2032-2047, October.
    2. Hazen, Benjamin T. & Boone, Christopher A. & Ezell, Jeremy D. & Jones-Farmer, L. Allison, 2014. "Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications," International Journal of Production Economics, Elsevier, vol. 154(C), pages 72-80.
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