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

Authors
Pomponio, Laura Matilde; Le Goc, Marc
Publication Year
2014
Language
English
Format
article
Status
Published version
Description
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.
Fil: Pomponio, Laura Matilde. Laboratoire des Sciences de l; Francia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; Argentina
Fil: Le Goc, Marc. Laboratoire des Sciences de l; Francia
Subject
METHODOLOGIES AND TOOLS
DATA AND KNOWLEDGE
KNOWLEDGE ENGINEERING
KNOWLEDGE MODELLING
Ciencias de la Computación
Ciencias de la Computación e Información
CIENCIAS NATURALES Y EXACTAS
Access level
Restricted access
License
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repository
CONICET Digital (CONICET)
Institution
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identifier
oai:ri.conicet.gov.ar:11336/15164