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Semantic information and knowledge integration through argumentative reasoning to support intelligent decision making

Author

Listed:
  • Naeem Khalid Janjua

    (Curtin University)

  • Farookh Khadeer Hussain

    (University of Technology Sydney)

  • Omar Khadeer Hussain

    (Curtin University)

Abstract

The availability of integrated, high quality information is a pre-requisite for a decision support system (DSS) to aid in the decision-making process. The introduction of semantic web ensures the seamless integration of information derived from diverse sources and transforms the DSS to an adoptable and flexible Semantic Web-DSS (Web-DSS). However, due to the monotonic nature of the layered development of semantic web, it lacks the capability to represent, reason and integrate incomplete and conflicting information. This, in turn, renders an enterprise incapable of knowledge integration; that is, integration of information about a subject that could potentially be incomplete, inconsistent and distributed among different Web-DSS within or across enterprises. In this article, we address the issues of incomplete and inconsistent semantic information and knowledge integration by using argumentation and argumentation schemes. We discuss the Argumentation-enabled Information Integration Web-DSS (Web@IDSS) along with its syntax and semantics for semantic information integration, and devise a methodology for sharing the results of Web@IDSS in Argument Interchange Format (AIF) format. We also discuss Argumentation-enabled Knowledge Integration Web-DSS (Web@KIDSS) for semantic knowledge integration. We provide formal syntax and semantics for the Web@KIDSS, propose a conceptual framework, and describe it in detail. We present the algorithms for knowledge integration and the prototype application for validation of results.

Suggested Citation

  • Naeem Khalid Janjua & Farookh Khadeer Hussain & Omar Khadeer Hussain, 2013. "Semantic information and knowledge integration through argumentative reasoning to support intelligent decision making," Information Systems Frontiers, Springer, vol. 15(2), pages 167-192, April.
    • Handle: RePEc:spr:infosf:v:15:y:2013:i:2:d:10.1007_s10796-012-9365-x
    DOI: 10.1007/s10796-012-9365-x
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    References listed on IDEAS

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    1. Alex Norta & Rik Eshuis, 2010. "Specification and verification of harmonized business-process collaborations," Information Systems Frontiers, Springer, vol. 12(4), pages 457-479, September.
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    3. Kweku-Muata Osei-Bryson & Ojelanki Ngwenyama, 2008. "Decision models for information systems management," Information Systems Frontiers, Springer, vol. 10(3), pages 277-279, July.
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    Cited by:

    1. Claudia Diamantini & Paolo Lo Giudice & Domenico Potena & Emanuele Storti & Domenico Ursino, 2021. "An Approach to Extracting Topic-guided Views from the Sources of a Data Lake," Information Systems Frontiers, Springer, vol. 23(1), pages 243-262, February.
    2. Qi Liu & Gengzhong Feng & Nengmin Wang & Giri Kumar Tayi, 2018. "A multi-objective model for discovering high-quality knowledge based on data quality and prior knowledge," Information Systems Frontiers, Springer, vol. 20(2), pages 401-416, April.
    3. Bexy Alfonso & Vicente Botti & Antonio Garrido & Adriana Giret, 2014. "A MAS-based infrastructure for negotiation and its application to a water-right market," Information Systems Frontiers, Springer, vol. 16(2), pages 183-199, April.
    4. Gürdal Ertek & Gül Tokdemir & Mete Sevinç & Murat Mustafa Tunç, 2017. "New knowledge in strategic management through visually mining semantic networks," Information Systems Frontiers, Springer, vol. 19(1), pages 165-185, February.
    5. Gabriele Kotsis & Ismail Khalil, 2013. "Special issue on Semantic Information Management guest editorial," Information Systems Frontiers, Springer, vol. 15(2), pages 151-157, April.
    6. Claudia Diamantini & Paolo Lo Giudice & Domenico Potena & Emanuele Storti & Domenico Ursino, 0. "An Approach to Extracting Topic-guided Views from the Sources of a Data Lake," Information Systems Frontiers, Springer, vol. 0, pages 1-20.
    7. Qi Liu & Gengzhong Feng & Nengmin Wang & Giri Kumar Tayi, 0. "A multi-objective model for discovering high-quality knowledge based on data quality and prior knowledge," Information Systems Frontiers, Springer, vol. 0, pages 1-16.

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