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Methods for knowledge acquisition
Abstract
The expert systems technology has been improving continuous up to the level where the cognitician and the expert can be assisted in doing their work by environments and automated programs generated and specialized instruments. in order to build an expert system, the cognitician must develop a certain program able to emulate the activity of an expert in solving the occurred problems. The system should reproduce as accurate as possible the expert's performance. The power of expert systems to solve the problems derives especially from the owned knowledge and less from inferential used mechanisms.
... The information gathering could be deductively from the human experts or inductively by learning from examples. The knowledge acquisition represents the extracting, structuring and organizing process of knowledge, out of one or many sources, so that the solving expertise of a matter can be stored in an expert system, in order to be used in solving the issues [9].The method of knowledge acquisition can be divided into manual, semi-automated and automated [8,9]. ...
... The information gathering could be deductively from the human experts or inductively by learning from examples. The knowledge acquisition represents the extracting, structuring and organizing process of knowledge, out of one or many sources, so that the solving expertise of a matter can be stored in an expert system, in order to be used in solving the issues [9].The method of knowledge acquisition can be divided into manual, semi-automated and automated [8,9]. ...
... It does not address the entire structure of FES. As such, the limitations and assumptions of FARME-D are all directed from the principle that governs the practice of automated knowledge acquisition and fuzzy association rule mining processes [9,22]. The limitations are meant to provide a guide on how the knowledge acquired could be managed to enhance the ES knowledge-base. ...
Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to knowledge acquisition is proposed in this paper as a viable solution to the challenges of rule-based unwieldiness and sharp boundary problem in building a fuzzy rule-based expert system. The fuzzy models were based on domain experts' opinion about the data description. The proposed approach is committed to modelling of a compact Fuzzy Rule-Based Expert Systems. It is also aimed at providing a platform for instant update of the knowledge-base in case new knowledge is discovered. The insight to the new approach strategies and underlining assumptions, the structure of FARME-D and its practical application in medical domain was discussed. Also, the modalities for the validation of the FARME-D approach were discussed.
... There are several methods and techniques for knowledge acquisition; in the following paragraph we will summarize main methods: [14, 20, 21, 16, 23, 24] Manual methods: in this method the knowledge engineer extracts expert knowledge and then codes it in a suitable format. There are many known manual methods like interviewing, process tracking, protocol analysis, observation, case analysis, critical incident analysis, discussions with the users, commentaries, and brainstorming. ...
In the artificial intelligence field, knowledge acquisition and reasoning are important areas for intelligent systems, especially knowledge base systems and expert systems. Knowledge acquisition is not an easy task since transferring expert knowledge required different methodologies and techniques based on expert domain, type of knowledge, knowledge engineer and expert domain. The success of the project depends on good knowledge management (KM). This paper presents a framework for manual knowledge acquisition. The proposed framework is for both types of knowledge (tacit and explicit). Using proposed framework allow organization to acquire knowledge from experts and get useful from it.
Knowledge plays an important role in designing intelligent systems especially in
expert systems and knowledge-based systems in different application domains. Its
efficiency and effectiveness depends on the Knowledge Acquisition (KA) phase. Acquiring
experience knowledge and transferring it into a knowledge-based system is complex and
involves a range of diverse activities. There are two key problems related to KA-the
identification of domain knowledge expertise and the heuristics that are useful in problem
solving; and the identification of people who can provide this knowledge. In this paper, an
approach for Web-based knowledge acquisition system is presented for acquiring expertise
from software designers. This knowledge is used in software design decision making and
knowledge reasoning. The process and decision laddering techniques are involved with the
concept mapping to create and evaluate the knowledge as efficient and effective as
possible.
The evolution of a software development life cycle (SDLC) by the data processing (DP) community was principally an attempt to ensure that software systems could be built which were accurate, reliable and maintainable. If knowledge-based systems (KBSs) are to become an established technology within the DP industry an SDLC for their development will also have to be evolved which can ensure that the standards expected in commercial and industrial software are met by future KBSs.This paper describes a practical approach to building KBSs and, in particular, how the development of hybrid systems, containing both conventional and knowledge-based components, can be managed effectively.