A knowledge sorting process for a product design expert system

Abstract

Knowledge sorting is one way to organize the knowledge acquired from the domain expert(s) and various sources. Well-organized knowledge representation will make rule extraction much easier. This paper describes a knowledge sorting process that was developed to facilitate the rule extraction for a product design expert system. The process capitalizes on the relationships between design attributes and factors, dependent and independent variables, and consists of three stages: identification of knowledge sources, generation of taxonomic trees, and organization of acquired knowledge. An example applies the sorting process to the development of an expert system for the design of wood head golf clubs.

An investigation into sustainable product conceptualization using a design knowledge hierarchy and Hopfield network

“…Whereas function-behavior-structure (FBS) method (Gero, 1990) and design information framework (DIF) method (Lim & Sato, 2003) are frequently deployed using network structures. Thus, a simplified process of graph decomposition for product design approaches (Chen & Occeña, 1999) often tends to transform network structure into hierarchical representation because the network structure has exactly the same psychological force as the hierarchy and can be transformed to the hierarchy or matrix (Novick & Hurley, 2001). Other advantages of employing hierarchical representation for product platform include simple structure, fast recording, and suitability for quantitative reasoning .…”

Product concept generation and selection using sorting technique and fuzzy c-means algorithm

“…In this regard, Module 1 of the PCGS, initial product platform formation, employs a well-established knowledge or requirements acquisition technique known as general sorting (Benfer, Brent, & Furbee, 1991). Sorting techniques, a 'contrived' type, were originally derived from Kelly's personal construct theory (Kelly, 1955), and have been applied and improved for product conceptualization with increasing frequency in recent years (Chen & Occeñ a, 1999;Chen, Khoo, & Yan, 2005;Yan, Pritchard, Chen, & Khoo, 2006). It was claimed that the 'contrived' type refers to techniques that are not so heavily dependent on natural language dialogue and very focused on the types of knowledge to elicit (McGeorge & Rugg, 1992).…”

Lecturers’ vs. students’ perceptions of the accessibility of instructional materials

“…These were tabulated by representation type, that is factor (feature) against attribute (construct) (see Chen & Occen˜a, 1999). The frequencies for these were grouped by novices' dichotomous view of representation as visual and textual.…”