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

First International Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data

Mathieu d’Aquina, Gabriel Kronbergerb, and Mari Carmen Suárez-Figueroac

Others  :  http://ceur-ws.org/Vol-868/paper3.pdf
PID  :  43556

来源: CEUR

【 摘 要 】

In this position paper, we claim that the need for time consuming da-ta preparation and result interpretation tasks in knowledge discovery, as well as for costly expert consultation and consensus building activities required for on-tology 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 rea-soning, 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.Keywords: data mining, ontology engineering, linked data, ontologies