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Using Linked Data traversal to label academic communities

Tiddi, Ilaria; d'Aquin, Mathieu and Motta, Enrico (2015). Using Linked Data traversal to label academic communities. In: Semantics, Analytics, Visualisation: Enhancing Scholarly Data Workshop co-located with the 24th International World Wide Web Conference, 19 May 2015, Florence, Italy, ACM.

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In this paper we exploit knowledge from Linked Data to ease the process of analysing scholarly data. In the last years, many techniques have been presented with the aim of analysing such data and revealing new, unrevealed knowledge, generally presented in the form of “patterns”. How-ever, the discovered patterns often still require human interpretation to be further exploited, which might be a time and energy consuming process. Our idea is that the knowledge shared within Linked Data can actuality help and ease the process of interpreting these patterns. In practice, we show how research communities obtained through standard network analytics techniques can be made more understand- able through exploiting the knowledge contained in Linked Data. To this end, we apply our system Dedalo that, by performing a simple Linked Data traversal, is able to automatically label clusters of words, corresponding to topics of the different communities.

Item Type: Conference or Workshop Item
Copyright Holders: 2015 International World Wide Web Conference Committee (IW3C2).
ISBN: 1-4503-3473-3, 978-1-4503-3473-0
Extra Information: co-located with the 24th International World Wide Web Conference
May 19, 2015 (full-day) - Florence, Italy
Keywords: Linked Data; educational data; community detection
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)
Related URLs:
Item ID: 42564
Depositing User: Kay Dave
Date Deposited: 22 Apr 2015 15:15
Last Modified: 08 May 2019 22:20
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