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

36 Pages Posted: 6 Jul 2018 First Look: Accepted

See all articles by Petar Ristoski

Petar Ristoski

University of Mannheim - Data and Web Science Group

Heiko Paulheim

University of Mannheim - Data and Web Science Group

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.

Keywords: Linked Open Data, Semantic Web, Data Mining, Knowledge Discovery

Suggested Citation

Ristoski, Petar and Paulheim, Heiko, Semantic Web in Data Mining and Knowledge Discovery: A Comprehensive Survey (2016). Journal of Web Semantics First Look 36_0_1. Available at SSRN: https://ssrn.com/abstract=3199217 or http://dx.doi.org/10.2139/ssrn.3199217

Petar Ristoski (Contact Author)

University of Mannheim - Data and Web Science Group ( email )

L 5, 2 - 2. OG
68161 Mannheim
Germany

Heiko Paulheim

University of Mannheim - Data and Web Science Group ( email )

L 5, 2 - 2. OG
68161 Mannheim
Germany

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