Semantic Web in data mining and knowledge discovery: A comprehensive survey

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• 作者：Petar Ristoski ; ^{petar.ristoski@informatik.uni-mannheim.de" class="auth_mail" title="E-mail the corresponding author} ; Heiko Paulheim ^{heiko@informatik.uni-mannheim.de" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Linked Open Data ; Semantic Web ; Data mining ; Knowledge discovery
• 刊名：Web Semantics: Science, Services and Agents on the World Wide Web
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：36
• 期：Complete
• 页码：1-22
• 全文大小：945 K

文摘

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.