Integrating Knowledge Engineering with Knowledge Discovery in Database: TOM4D and TOM4L

Book Chapter

Authorship

POMPONIO, LAURA MATILDE ; Le Goc, Marc

Date

2014

Publishing House and Editing Place

Springer

Book

Innovations in Intelligent Machines-4. Recent Advances in Knowledge Engineering (pp. 189-231)
Springer

ISBN

978-3-319-01865-2

Summary Information provided by the agent in 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.