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

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Innovations in Intelligent Machines-4

Part of the book series: Studies in Computational Intelligence ((SCI,volume 514))

Abstract

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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Notes

  1. 1.

    The symbol \( \triangleq \) denotes rewriting or “corresponds to”.

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Authors and Affiliations

  1. Laboratoire de Sciences de l’Information et des Systèmes (LSIS), UMR CNRS 7296, Aix-Marseille University, Domaine universitaire de Saint Jérôme, Avenue Escadrille Normandie Niemen, 13397, Marseille, France

    Laura Pomponio & Marc Le Goc

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  1. Laura Pomponio
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  2. Marc Le Goc
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Correspondence to Laura Pomponio .

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Editors and Affiliations

  1. Ecole Polytechnique Universitaire de Marseille, Aix-Marseille University (AMU), Marseille, France

    Colette Faucher

  2. Faculty of Education, Science, Technology & Mathematics, University of Canberra, Canberra, Australia

    Lakhmi C. Jain

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Pomponio, L., Le Goc, M. (2014). Integrating Knowledge Engineering with Knowledge Discovery in Database: TOM4D and TOM4L. In: Faucher, C., Jain, L. (eds) Innovations in Intelligent Machines-4. Studies in Computational Intelligence, vol 514. Springer, Cham. https://doi.org/10.1007/978-3-319-01866-9_6

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  • DOI: https://doi.org/10.1007/978-3-319-01866-9_6

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  • Online ISBN: 978-3-319-01866-9

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