Ontology-Driven KDD Process Composition

Authors:
Claudia Diamantini;Domenico Potena;Emanuele Storti
Affiliations:
Dipartimento di Ingegneria Informatica, Gestionale e dell'Automazione "M. Panti", Università Politecnica delle Marche, Ancona, Italy 60131;Dipartimento di Ingegneria Informatica, Gestionale e dell'Automazione "M. Panti", Università Politecnica delle Marche, Ancona, Italy 60131;Dipartimento di Ingegneria Informatica, Gestionale e dell'Automazione "M. Panti", Università Politecnica delle Marche, Ancona, Italy 60131
Venue:
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Year:
2009

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Abstract

One of the most interesting challenges in Knowledge Discovery in Databases (KDD) field is giving support to users in the composition of tools for forming a valid and useful KDD process. Such an activity implies that users have both to choose tools suitable to their knowledge discovery problem, and to compose them for designing the KDD process. To this end, they need expertise and knowledge about functionalities and properties of all KDD algorithms implemented in available tools. In order to support users in this heavy activity, in this paper we introduce a goal-driven procedure for automatically compose algorithms. The proposed procedure is based on the exploitation of KDDONTO, an ontology formalizing the domain of KDD algorithms, allowing us to generate valid and non-trivial processes.