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Towards knowledge modeling and manipulation technologies: A survey

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  • Bimba, Andrew Thomas
  • Idris, Norisma
  • Al-Hunaiyyan, Ahmed
  • Mahmud, Rohana Binti
  • Abdelaziz, Ahmed
  • Khan, Suleman
  • Chang, Victor

Abstract

A system which represents knowledge is normally referred to as a knowledge based system (KBS). This article focuses on surveying publications related to knowledge base modelling and manipulation technologies, between the years 2000–2015. A total of 185 articles excluding the subject descriptive articles which are mentioned in the introductory parts, were evaluated in this survey. The main aim of this study is to identify different knowledge base modelling and manipulation techniques based on 4 categories; 1) linguistic knowledge base; 2) expert knowledge base; 3) ontology and 4) cognitive knowledge base. This led to the proposition of 8 research questions, which focused on the different categories of knowledge base modelling technologies, their underlying theories, knowledge representation technique, knowledge acquisition technique, challenges, applications, development tools and development languages. A part of the findings from this survey is the high dependence of linguistic knowledge base, expert knowledge base and ontology on volatile expert knowledge. A promising technique for knowledge-based business management and other knowledge related applications is also discussed.

Suggested Citation

  • Bimba, Andrew Thomas & Idris, Norisma & Al-Hunaiyyan, Ahmed & Mahmud, Rohana Binti & Abdelaziz, Ahmed & Khan, Suleman & Chang, Victor, 2016. "Towards knowledge modeling and manipulation technologies: A survey," International Journal of Information Management, Elsevier, vol. 36(6), pages 857-871.
    • Handle: RePEc:eee:ininma:v:36:y:2016:i:6:p:857-871
    DOI: 10.1016/j.ijinfomgt.2016.05.022
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    References listed on IDEAS

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    1. Yingxu Wang, 2008. "On Concept Algebra: A Denotational Mathematical Structure for Knowledge and Software Modeling," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 2(2), pages 1-19, April.
    2. Yingxu Wang, 2007. "The OAR Model of Neural Informatics for Internal Knowledge Representation in the Brain," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 1(3), pages 66-77, July.
    3. Chris Kimble & José Braga Vasconcelos & Álvaro Rocha, 2016. "Competence management in knowledge intensive organizations using consensual knowledge and ontologies," Information Systems Frontiers, Springer, vol. 18(6), pages 1119-1130, December.
    4. Rehman, Muhammad Habib ur & Chang, Victor & Batool, Aisha & Wah, Teh Ying, 2016. "Big data reduction framework for value creation in sustainable enterprises," International Journal of Information Management, Elsevier, vol. 36(6), pages 917-928.
    5. Yingxu Wang, 2008. "On System Algebra: A Denotational Mathematical Structure for Abstract System Modeling," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 2(2), pages 20-43, April.
    6. Shang-Ming Zhou & Ronan A Lyons & Sinead Brophy & Mike B Gravenor, 2012. "Constructing Compact Takagi-Sugeno Rule Systems: Identification of Complex Interactions in Epidemiological Data," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-14, December.
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    1. Ni, Zi-jian & Rong, Lili & Wang, Ning & Cao, Shuo, 2019. "Knowledge model for emergency response based on contingency planning system of China," International Journal of Information Management, Elsevier, vol. 46(C), pages 10-22.

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