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OntoDM-KDD: Ontology for Representing the Knowledge Discovery Process

  • Panče Panov
  • Larisa Soldatova
  • Sašo Džeroski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8140)

Abstract

In this article, we present an ontology for representing the knowledge discovery (KD) process based on the CRISP-DM process model (OntoDM-KDD). OntoDM-KDD defines the most essential entities for describing data mining investigations in the context of KD in a two-layered ontological structure. The ontology is aligned and reuses state-of-the-art resources for representing scientific investigations, such as Information Artifact Ontology (IAO) and Ontology of Biomedical Investigations (OBI). It provides a taxonomy of KD specific actions, processes and specifications of inputs and outputs. OntoDM-KDD supports the annotation of DM investigations in application domains. The ontology has been thoroughly assessed following the best practices in ontology engineering, is fully interoperable with many domain resources and easily extensible. OntoDM-KDD is available at http://www.ontodm.com .

Keywords

Knowledge Discovery in Databases CRISP-DM Data Mining Investigation Data Mining Domain Ontology 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Panče Panov
    • 1
  • Larisa Soldatova
    • 2
  • Sašo Džeroski
    • 1
  1. 1.Jožef Stefan InstituteLjubljanaSlovenia
  2. 2.Brunel UniversityLondonUnited Kingdom

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