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Inductive Databases and Constraint-Based Data Mining pp 27–58Cite as

Representing Entities in the OntoDM Data Mining Ontology

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Abstract

Motivated by the need for unification of the domain of data mining and the demand for formalized representation of outcomes of data mining investigations, we address the task of constructing an ontology of data mining. Our heavy-weight ontology, named OntoDM, is based on a recently proposed general framework for data mining. It represent entites such as data, data mining tasks and algorithms, and generalizations (resulting from the latter), and allows us to cover much of the diversity in data mining research, including recently developed approaches to mining structured data and constraint-based data mining. OntoDM is compliant to best practices in ontology engineering, and can consequently be linked to other domain ontologies: It thus represents a major step towards an ontology of data mining investigations.

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Author information

Authors and Affiliations

  1. Jožef Stefan Institute, Jamova cesta 39, 1000, Ljubljana, Slovenia

    Panče Panov & Sašo Džeroski

  2. Aberystwyth University, Penglais, Aberystwyth, SY23 3DB, Wales, UK

    Larisa N. Soldatova

Authors
  1. Panče Panov
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  2. Sašo Džeroski
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  3. Larisa N. Soldatova
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Corresponding author

Correspondence to Panče Panov .

Editor information

Editors and Affiliations

  1. , Department of Knowledge Technologies, Jozef Stefan Institute, Jamova 39, Ljubljana, 1000, Slovenia

    Sašo Džeroski

  2. , Mathematics and Computer Science, University of Antwerp, Middelheimlaan 1, Antwerpen, B-2020, Belgium

    Bart Goethals

  3. , Dept. of Knowledge Technologies, Jožef Stefan Institute, Jamova cesta 39, Ljubljana, SI-1000, Slovenia

    Panče Panov

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Panov, P., Džeroski, S., Soldatova, L.N. (2010). Representing Entities in the OntoDM Data Mining Ontology. In: Džeroski, S., Goethals, B., Panov, P. (eds) Inductive Databases and Constraint-Based Data Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7738-0_2

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  • DOI: https://doi.org/10.1007/978-1-4419-7738-0_2

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