Extending the Reach of Knowledge Engineering Practice beyond the Frontiers of Formal Sectors Using a Feature and Function Based Classification
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Abstract
Abstract: In recent years, there had been many frameworks on improving Knowledge Engineering (KE). These frameworks had emerged for reasons ranging from shortening KE product development time, performance improvement, to addressing knowledge acquisition challenges. Despite these, impact of KE products (Expert Systems (ES) and Knowledge Based Systems (KBS)) has not been felt in non-formal sectors where there is no formalized way of keeping and exchanging their specialized knowledge. Here, heuristics and implicit tacit knowledge are often used to solve significant problems. When custodians of such specialized knowledge die, their untapped problem solving skills perish with them. Such is common in Africa, especially among native farmers, hunters and healers. Extending the reach of KE practice to these sectors can effectively help overcome this challenge. This however requires a better understanding of existing KE frameworks in a bid to isolate gaps responsible for this oversight and factor solution to this problem into subsequent design of a KE framework. To this end, this paper provides a concise feature and function based classification and review of eight prominent KE frameworks and models (CommonKADS, MIKE, MOKA, PROTÉGÉ II, SPEDE, RLM, CRLM, and PGM/CPGM) in common use in recent years and consequently recommends development of a KE framework that addresses the isolated problem.
Keywords: Expert Systems, Non-formal Sectors, Knowledge Engineering, Specialized Knowledge, Domain Knowledge CustodiansDownloads
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References
M. Rouse, and M. Haughn, (2017) Definition of Knowledge Engineering. Retrieved from
https://searchenterpriseai.techtarget.com/definition/knowledge-engineering
Gibbs, S.,l (2016). Autonomous Cars – Who is building them and How. Retrieved from
S. Rudi, V.R. Benjamins, and F. Dieter (2017). Knowledge Engineering: Principles
and Methods. Retrieved from https://www.researchgate.net/profile
J. Anga, (2014) Production of cocoa beans, Quarterly Bulletin of Cocoa Statistics,
London, 2006, XXXI, XXXIX (2013), XL (2014).
P. Sureephong, N. Chakpitak, Y. Ouzroute, G. Neubert and A. Bouras (2016)
Knowledge Engineering Techniques for Cluster Development. Retrieved from https://www.researchgate.net › publication › 301872111_Knowledge_Engin...
M. Shodhganga (2018). Agricultural Expert System. Retrieved from
http://shodhganga.inflibnet.ac.in/bitstream/10603/71534/10/10_chapter%203.pdf.
M. Kantureeva, A. Zakirova, T. Mannapova, M. Mussaif, and K. Nigmetov (2014).
The Methodology of Expert Systems. 14(2).
S. Smith, Expert System Architecture Block Diagram (2016) Retrieved from
https://www.google.com/search?q=expert+system+architecture+block+diagram
S. Osindero, Y.W. Teh, (2006). A fast Learning Algorithm for Fast Learning
Nets (PDF). Neural Computations 18(7), 1527–1554.
S.I. Ele, E.E. Umoh, and W.A. Adesola, (2014). An Overview of the
Development, Principles, Stages and Building Blocks of Expert System. West African Journal of Industrial & Academic Research 2(1)
N. Sriram and H. Philip (2018). Expert System for Decision Support in Agriculture
Retrieved from http://agritech.tnau.ac.in/pdf/14.pdf.
I. Atanasova, & J. Krupka (2013). Architecture and Design of Expert System for Quality
of Life Evaluation. Informatica Economicã , 17(3), 28-35.
F. Shervan, T. Hadi and J. Shahram (2013) Design and Development of an Expert
System to Help Head of University Departments in International Journal of Science and Modern Engineering (IJISME) 1(2), 45 – 48.
M.A. Saket, and D. Vikas (2014). Expert Systems In Agriculture: An Overview;
International Journal of Science Technology & Engineering. 1(5).
M. Diana-Aderina, & A. Mihai-Constantin. (2015) Architectural Model of Expert Systems. International Symposium Engineering and Competitiveness (EMC 2015). Retrieved from https://www.researchgate.net/publication/281268197_ARCHITECTURAL_MODEL_OF_EXPERT_SYSTEMS
D.A. Wiliyanto (2017). The use of Web based expert system application for
identification and intervention of children with special needs in inclusive schools. journal.uad.ac.id › Home › 11(4)
Santosa, Romla and Herawati, (2018) Expert System Diagnosis of Cataract Eyes Using Fuzzy Mamdani Method. Journal of Physics: Conference Series, 953(1).