Ontology-Based Knowledge Model for Multi-View KDD Process | Zendy

El Moukhtar Zemmouri | Zendy; Hicham Behja | Zendy; Abdelaziz Marzak | Zendy; Brigitte Trousse | Zendy

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Ontology-Based Knowledge Model for Multi-View KDD Process

Author(s) -

El Moukhtar Zemmouri,

Hicham Behja,

Abdelaziz Marzak,

Brigitte Trousse

Publication year - 2012

Publication title -

international journal of mobile computing and multimedia communications

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.193

H-Index - 10

eISSN - 1937-9404

pISSN - 1937-9412

DOI - 10.4018/jmcmc.2012070102

Subject(s) - computer science , knowledge extraction , viewpoints , ontology , process (computing) , domain knowledge , identification (biology) , knowledge modeling , set (abstract data type) , data mining , information retrieval , data science , artificial intelligence , art , philosophy , botany , epistemology , biology , visual arts , operating system , programming language

International audienceKnowledge Discovery in Databases (KDD) is a highly complex, iterative and interactive process that involves several types of knowledge and expertise. In this paper we propose to support users of a multi-view analysis (a KDD process held by several experts who analyze the same data with different viewpoints). Our objective is to enhance both the reusability of the process and coordination between users. To do so, we propose a formalization of viewpoint in KDD and a Knowledge Model that structures domain knowledge involved in a multi-view analysis. Our formalization, using OWL ontologies, of viewpoint notion is based on CRISP-DM standard through the identification of a set of generic criteria that characterize a viewpoint in KDD

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