Reducing the gap between experts' knowledge and data: The TOM4D methodology

LAURA POMPONIO; MARC LE GOC

DATA & KNOWLEDGE ENGINEERING

ELSEVIER SCIENCE BV

Lugar: NORTH-HOLLAND; Año: 2014 vol. 94 p. 1 - 37

0169-023X

Dynamic process modelling is generally accomplished from experts´ knowledge through Knowledge Engineering (KE); however, the obtained models are sometimes deficient for interpreting the input data flow coming from the real process evolution perceived through sensors. This shortcoming lies in specialists´ tacit knowledge, difficult to elicit, and in that certain process phenomena are unknown or unforeseen to experts. An alternative to complement the modelling task is to resort to a Knowledge Discovery in Database (KDD) process. Nevertheless, most KE approaches do not address the processing of knowledge obtained from data. This work proposes a KE methodology called Timed Observation Modelling For Diagnosis (TOM4D) which allows building dynamic process models from experts´ knowledge and data where the obtained models can be compared and combined with models obtained through a KDD process in order to define a model more suitable to the dynamic process reality.