Combining Data Mining and Ontology Engineering to Enrich Ontologies and Linked Data

Mathieu d'Aquin, Gabriel Kronberger, Mari Carmen Suárez-Figueroa

Research output: Chapter in Book/Report/Conference proceedingsConference contribution

8 Citations (Scopus)


In this position paper, we claim that the need for time consuming data preparation and result interpretation tasks in knowledge discovery, as well as for costly expert consultation and consensus building activities required for ontology building can be reduced through exploiting the interplay of data mining and ontology engineering. The aim is to obtain in a semi-automatic way new knowledge from distributed data sources that can be used for inference and reasoning, as well as to guide the extraction of further knowledge from these data sources. The proposed approach is based on the creation of a novel knowledge discovery method relying on the combination, through an iterative ‘feedback-loop’, of (a) data mining techniques to make emerge implicit models from data and (b) pattern-based ontology engineering to capture these models in reusable, conceptual and inferable artefacts.
Original languageEnglish
Title of host publicationProceedings of the First International Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data
Number of pages6
Publication statusPublished - 2012

Publication series

NameCEUR Workshop Proceedings
ISSN (Print)1613-0073


  • data mining
  • ontology engineering
  • linked data
  • ontologies
  • Linked data
  • Ontologies
  • Data mining
  • Ontology engineering


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