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Explaining clusters with inductive logic programming and linked data

Tiddi, Ilaria; d'Aquin, Mathieu and Motta, Enrico (2013). Explaining clusters with inductive logic programming and linked data. In: 12th International Semantic Web Conference, 21-25 Oct 2013, Sydney, Australia.

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Knowledge Discovery consists in discovering hidden regularities in large amounts of data using data mining techniques. The obtained patterns require an interpretation that is usually achieved using some background knowledge given by experts from several domains. On the other hand, the rise of Linked Data has increased the number of connected cross-disciplinary knowledge, in the form of RDF datasets, classes and relationships. Here we show how Linked Data can be used in an Inductive Logic Programming process, where they provide background knowledge for finding hypotheses regarding the unrevealed connections between items of a cluster. By using an example with clusters of books, we show how different Linked Data sources can be used to automatically generate rules giving an underlying explanation to such clusters.

Item Type: Conference or Workshop Item
Copyright Holders: 2013 The Authors
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Item ID: 39270
Depositing User: Kay Dave
Date Deposited: 15 Jan 2014 15:05
Last Modified: 09 May 2019 07:53
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