Semantic Web in Data Mining and Knowledge Discovery: A Comprehensive Survey

Petar Ristoski, Heiko Paulheim

Abstract


Data Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in that field are knowledge intensive and can often benefit from using additional knowledge from various sources. Therefore, many approaches have been proposed in this area that combine Semantic Web data with the data mining and knowledge discovery process. This survey article gives a comprehensive overview of those approaches in different stages of the knowledge discovery process. As an example, we show how Linked Open Data can be used at various stages for building content-based recommender systems. The survey shows that, while there are numerous interesting research works performed, the full potential of the Semantic Web and Linked Open Data for data mining and KDD is still to be unlocked.

Full Text: Untitled
Type of Paper: Survey Paper
Keywords: Linked Open Data, Semantic Web, Data Mining, Knowledge Discovery
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