IDEAS home Printed from https://ideas.repec.org/a/hur/ijarbs/v6y2016i9p1-18.html
   My bibliography  Save this article

Retrieving Information from Social Media using Ontology

Author

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
    as

    Download full text from publisher

    File URL: http://hrmars.com/hrmars_papers/Retrieving_Information_from_Social_Media_using_Ontology1.pdf
    Download Restriction: no

    File URL: http://hrmars.com/hrmars_papers/Retrieving_Information_from_Social_Media_using_Ontology1.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.
    2. Aaltonen, Aleksi Ville & Alaimo, Cristina & Kallinikos, Jannis, 2021. "The making of data commodities: data analytics as an embedded process," LSE Research Online Documents on Economics 110296, London School of Economics and Political Science, LSE Library.
    3. Ray Qing Cao & Dara G. Schniederjans & Vicky Ching Gu, 2021. "Stakeholder sentiment in service supply chains: big data meets agenda-setting theory," Service Business, Springer;Pan-Pacific Business Association, vol. 15(1), pages 151-175, March.
    4. Akhtar, Pervaiz & Khan, Zaheer & Tarba, Shlomo & Jayawickrama, Uchitha, 2018. "The Internet of Things, dynamic data and information processing capabilities, and operational agility," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 307-316.
    5. Pan Liu & Shu-ping Yi, 2018. "Investment decision-making and coordination of a three-stage supply chain considering Data Company in the Big Data era," Annals of Operations Research, Springer, vol. 270(1), pages 255-271, November.
    6. Benjamin T. Hazen & Joseph B. Skipper & Christopher A. Boone & Raymond R. Hill, 2018. "Back in business: operations research in support of big data analytics for operations and supply chain management," Annals of Operations Research, Springer, vol. 270(1), pages 201-211, November.
    7. Bin Shen & Hau-Ling Chan, 2017. "Forecast Information Sharing for Managing Supply Chains in the Big Data Era: Recent Development and Future Research," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(01), pages 1-26, February.
    8. César Martínez-Olvera & Jaime Mora-Vargas, 2019. "A Comprehensive Framework for the Analysis of Industry 4.0 Value Domains," Sustainability, MDPI, vol. 11(10), pages 1-21, May.
    9. Roßmann, Bernhard & Canzaniello, Angelo & von der Gracht, Heiko & Hartmann, Evi, 2018. "The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 135-149.
    10. Patrucco, Andrea S. & Marzi, Giacomo & Trabucchi, Daniel, 2023. "The role of absorptive capacity and big data analytics in strategic purchasing and supply chain management decisions," Technovation, Elsevier, vol. 126(C).
    11. Shahriar Akter & Samuel Fosso Wamba, 2019. "Big data and disaster management: a systematic review and agenda for future research," Annals of Operations Research, Springer, vol. 283(1), pages 939-959, December.
    12. Zhong, Ray Y. & Huang, George Q. & Lan, Shulin & Dai, Q.Y. & Chen, Xu & Zhang, T., 2015. "A big data approach for logistics trajectory discovery from RFID-enabled production data," International Journal of Production Economics, Elsevier, vol. 165(C), pages 260-272.
    13. Maya Vachkova & Arsalan Ghouri & Haidy Ashour & Normalisa Binti Md Isa & Gregory Barnes, 2023. "Big data and predictive analytics and Malaysian micro-, small and medium businesses," SN Business & Economics, Springer, vol. 3(8), pages 1-28, August.
    14. Cerchione, Roberto & Esposito, Emilio, 2016. "A systematic review of supply chain knowledge management research: State of the art and research opportunities," International Journal of Production Economics, Elsevier, vol. 182(C), pages 276-292.
    15. Kummitha, Rama Krishna Reddy, 2019. "Smart cities and entrepreneurship: An agenda for future research," Technological Forecasting and Social Change, Elsevier, vol. 149(C).
    16. Musson, Anne & Rousselière, Damien, 2020. "Identifying the impact of crisis on cooperative capital constraint. A short note on French craftsmen cooperatives," Finance Research Letters, Elsevier, vol. 35(C).
    17. Yadegaridehkordi, Elaheh & Hourmand, Mehdi & Nilashi, Mehrbakhsh & Shuib, Liyana & Ahani, Ali & Ibrahim, Othman, 2018. "Influence of big data adoption on manufacturing companies' performance: An integrated DEMATEL-ANFIS approach," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 199-210.
    18. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Papadopoulos, Thanos & Luo, Zongwei & Wamba, Samuel Fosso & Roubaud, David, 2019. "Can big data and predictive analytics improve social and environmental sustainability?," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 534-545.
    19. Biman Darshana Hettiarachchi & Stefan Seuring & Marcus Brandenburg, 2022. "Industry 4.0-driven operations and supply chains for the circular economy: a bibliometric analysis," Operations Management Research, Springer, vol. 15(3), pages 858-878, December.
    20. Purva Grover & Arpan Kumar Kar, 2017. "Big Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 203-229, September.

    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:hur:ijarbs:v:6:y:2016:i:9:p:1-18. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Hassan Danial Aslam (email available below). General contact details of provider: http://hrmars.com/index.php/pages/detail/IJARBSS .

    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.