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Issues in Structuring the Knowledge-base of Expert Systems
Eric C. Okafor1 and Charles C. Osuagwu2
1Department of Computer Engineering, Enugu State University of Science and Technology, Enugu, Nigeria
2Department of Electronic Engineering, University of Nigeria, Nsukka, Nigeria
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The major bottlenecks in expert system development lie within the processes of eliciting and representing knowledge. Knowledge representation schemes combine data structures, and interpretative procedures that enable extracting the knowledge embedded in the data structures. A broad spectrum of knowledge types need to be represented, but available representation techniques are not optimum systems since they vary in level of expressiveness and power.
Knowledge demands more than the conventional representation structures used for databases and information. This is because information is derived from processing, refining and analyzing raw data. The extra refinement, analysis and addition of heuristics to information convert it to knowledge.
Different knowledge representation formalisms have emerged and the drive towards efficient knowledge representation has also led to the development of knowledge representation languages like PROLOG, KRYPTON, and KL-1. The choice of a KR technique is essentially dependent on a range of factors. By defining the characteristics of a given representation technique and determining their ability to handle the functional equivalence of a specific knowledge area, we can determine whether it is a suitable match.
In this paper, we build up current issues in structuring knowledgebases by first determining the features of an efficient KR system and factors that influence their expressiveness and power. Then, some KR techniques (rules, semantic networks, frames, logic) hitherto considered effective for structuring knowledgebases are presented and critically appraised to determine suitable application area(s).
Keywords:
Knowledge representation, knowledgebase, production rule, semantic nets, frames, propositional logic, predicate logic, fuzzy logic.
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