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Title: Developing knowledge-based systems through ontology mapping and ontology guided knowledge acquisition
Author: Corsar, David
ISNI:       0000 0004 2672 6854
Awarding Body: University of Aberdeen
Current Institution: University of Aberdeen
Date of Award: 2009
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This thesis focuses on reusing domain ontologies and generic problem solvers (PSs) in the development of new Knowledge Based Systems (KBSs). A two-stage methodology for achieving this has been developed: in the first stage, knowledge is mapped from a domain ontology to the requirements of a generic PS (expressed in a PS ontology); in the second stage, this mapped knowledge and the domain specific reasoning requirements of the generic PS are used to “drive” by the PS. This acquired knowledge can then be used to generate an executable KBS. Developing this methodology involved a detailed review of the earlier reuse literature, in order to understand the strengths and weaknesses of earlier approaches. Generic PSs for propose-and-revise design and diagnosis were also developed based on two existing KBSs which performed these tasks in the elevator domain. To gain insights into the KBS development process, the generic PSs were used to manually build two new executable KBSs. A tool MAKTab, was then developed to support the methodology by semi-automatically performing the actions undertaken during the manual building of the two KBSs. MAKTab has been used to successfully recreate the two elevator systems, and fully develop diagnosis and design KBSs in the computer hardware domain. The findings described in the thesis support the belief that a domain ontology developed for one type of PS will, in general, be unable to fully meet the procedural requirements of another type of PS; this knowledge must therefore be acquired. This work also shows that a single, general knowledge acquisition technique can be applied with different types of generic PSs, to acquire the necessary procedural knowledge.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Expert systems (Computer science) ; Artificial Intelligence