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Walking Linked Data: a graph traversal approach to explain clusters

Tiddi, Ilaria; d'Aquin, Mathieu and Motta, Enrico (2014). Walking Linked Data: a graph traversal approach to explain clusters. In: 5th International Workshop on Consuming Linked Data (COLD 2014), 20 Oct 2014, Riva del Garda, Italy.

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Link traversal is one of the biggest advantages of Linked Data, as it allows the serendipitous discovery of new knowledge thanks to the natural connections between data of different sources. Our general problem is to understand how such a property can benefit the Knowledge Discovery process: in particular, we aim at using Linked Data to explain the patterns of data that have been extracted from a typical data min- ing process such as clustering. The strategy we propose here is Linked Data traversal, in which we explore and build on-the-fly an unknown Linked Data graph by simply deferencing entities’ URIs until we find, by following the links between entities, a valid explanation to our clusters. The experiments section gives an insight into the performance of such an approach, in terms of time and scalability, and show how the links easily gather knowledge from different data sources.

Item Type: Conference or Workshop Item
Copyright Holders: 2014 The Authors
Extra Information: co-located with the 13th International Semantic Web Conference (ISWC 2014)
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)
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Item ID: 41683
Depositing User: Kay Dave
Date Deposited: 16 Jan 2015 09:30
Last Modified: 10 May 2019 07:46
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