Expertise transfer and complex problems: using AQUINAS as a knowledge-acquisition workbench for knowledge-based systems
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Cited by (164)
Developing a Capability to Elicit and Structure Psychosocial Decision Information within Computational Models
2015, Procedia ManufacturingA questioning based method to automatically acquire expert assembly diagnostic knowledge
2014, CAD Computer Aided DesignCitation Excerpt :Similarly, the KBDT system [23] takes in an initial set of vocabulary terms, acronyms and euphemisms, and works with examples to elicit design rationale. An early system ACQUINAS [24] also looks at knowledge for configuring systems using rating grids, and again uses examples—but this is applicable for domains with a prior, clear, domain model. Another research effort that targets similar objectives uses Fuzzy Cognitive Models [25] as the framework for modeling diagnostic knowledge.
User expertise in contemporary information systems: Conceptualization, measurement and application
2013, Information and ManagementCitation Excerpt :In addition, numerous studies have identified that knowledge sharing is an essential part of knowledge management, and it is imperative to ES success [e.g. 99, 100]. Studies of several disciplines; IS [e.g. 101, 102], business [e.g. 103], and psychology [e.g. 104, 105, 106]; suggest that experts, are willing, able and motivated to convey their superior knowledge and skills to novices. The test of the structural model includes estimates of the path coefficient, which indicate the strength of the relationship between the independent and dependent variable, and the R2 values, which represent the amount of variance explained by the independent variable/s. Together, the R2 and the path coefficient (loadings and significance) indicate how well the data supports the hypothesized model [91].
From knowledge science to symbiosis science
2013, International Journal of Human Computer StudiesCitation Excerpt :In 1985, Brian Gaines produced an early version of a diagram predicting the future of knowledge systems, and showing Wisdom as the pinnacle of that evolution (Fig. 1). Four years later, I was honored to join Brian, along with my mentor and friend John Boose (Boose, 1986; Boose and Bradshaw, 1987; Bradshaw and Boose, 1990; Bradshaw et al., 1991; Shema et al., 1990), in sending out a call for papers for a Workshop on Wisdom-Based Systems that was to take place on June 22–24, 1989 at the Rosario Resort on Orcas Island in Washington state. Here is a paragraph from that call:
Failure analysis expert system for onshore pipelines. Part - I: Structured database and knowledge acquisition
2011, Expert Systems with ApplicationsCitation Excerpt :These techniques have been developed to help DE the reason about his expertise, and the KE to understand it. Some of the techniques mainly for data extraction are: Repertory Grid Analysis (RGA); (Boose, 1985; Gaines & Shaw, 1980), and its derivations, such as VODKA (Tseng & Lin, 2009); KAMET (Chu & Hwang, 2008), ETS (Boose, 1985), AQUINAS (Boose & Bradshaw, 1986); KITTEN (Shaw & Gaines, 1987) and others. Machine Learning Approach: In this case, the knowledge approximation from domain examples provided by the experts and using software with learning algorithms, for example ID3 (Quinlan, 1986) and AQ11 (Michalski & Chilausky, 1980) which are two well-known commercial learning algorithms.
VODKA: Variant objects discovering knowledge acquisition
2009, Expert Systems with Applications