Producción CyT
Capítulo de Libro
Autoría
POMPONIO, LAURA MATILDE
;
Le Goc, Marc
Fecha
2014
Editorial y Lugar de Edición
Springer
Libro
Innovations in Intelligent Machines-4. Recent Advances in Knowledge Engineering
(pp. 189-231)
Springer
Springer
ISBN
978-3-319-01865-2
Resumen
Información suministrada por el agente en
SIGEVA
Knowledge Engineering (KE) provides resources to build a conceptual model from experts’ knowledge which is sometimes deficient to interpret the input data flow coming from a concrete process. On the other hand, data mining techniques in a process of Knowledge Discovery in Databases (KDD) can be used in order to obtain representative patterns of data which could allow to improve the model to be constructed. However, interpreting these patterns is difficult due to the gap which exists betwe...
Knowledge Engineering (KE) provides resources to build a conceptual model from experts’ knowledge which is sometimes deficient to interpret the input data flow coming from a concrete process. On the other hand, data mining techniques in a process of Knowledge Discovery in Databases (KDD) can be used in order to obtain representative patterns of data which could allow to improve the model to be constructed. However, interpreting these patterns is difficult due to the gap which exists between the expert’s conceptual universe and that of the process instrumentation. This chapter proposes then a global approach which combines KE with KDD in order to allow the construction of Knowledge Models for Knowledge Based Systems from expert knowledge and knowledge discovered in data. This approach is grounded in the Theory of Timed Observations on which both a KE methodology and a KDD process are based, so that the resulting models can be compared.
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Palabras Clave
KNOWLEDGE DISCOVERY IN DATABASEKNOWLEDGE ENGINEERINGDYNAMIC PROCESS MODELLING
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