Ontology-Guided Intelligent Data Mining Assistance: Combining Declarative and Procedural Knowledge

M. Charest and S. Delisle (Canada)

Keywords

Intelligent Systems, Knowledge Acquisition, Knowledge Representation, Ontologies, CaseBased Reasoning, Data Mining.

Abstract

The effective application of a data mining process is littered with many difficult and technical decisions (i.e. data cleansing, feature transformations, algorithms, parameters, evaluation). Subsequently, most data mining products provide a large number of models and tools, but few provide intelligent assistance for addressing the above-mentioned challenges that face the non-specialist data miner. In this paper, we propose the realization of a hybrid intelligent data mining assistant, based on the synergistic combination of both declarative (Description Logic) and procedural (SWRL Rules) ontology knowledge in order to empower the non-specialist data miner throughout the key phases of the CRISP-DM data mining process.

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