Abstract
The Repertory Grid method is widely used in knowledge engineering to infer functional relationships between constructs given by an expert. The method is ignoring information that could be used to infer more precise dependencies. This paper proposes an improvement to take advantage on the information that is being ignored in the current method. Furthermore, this improvement fixes several other limitations attached to the original method, such as election in a discrete set of two values as a similarity pole or a contrast pole, the arbitrary measurement of distances, the unit-scale dependency and the normalization, among others. The idea is to use linear regression to estimate the correlation between constructs and use the fitness error as a distance measure.
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© 2008 International Federation for Information Processing
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Calot, E., Britos, P., García-Martínez, R. (2008). Optimizing Relationships Information in Repertory Grids. In: Bramer, M. (eds) Artificial Intelligence in Theory and Practice II. IFIP AI 2008. IFIP – The International Federation for Information Processing, vol 276. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09695-7_16
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DOI: https://doi.org/10.1007/978-0-387-09695-7_16
Publisher Name: Springer, Boston, MA
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