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A Linked Dataverse Knows Better: Boosting Recommendation Quality Using Semantic Knowledge

Authors:
Andreas Lommatzsch
Till Plumbaum
Sahin Albayrak

Keywords: linked open data; recommendation; semantic web; user profile enrichment; personalization

Abstract:
The advent of Linked Open Data (LOD) gave birth to a plethora of open datasets freely available to everyone. Accompanied with LOD, a new research field arises focusing on how to handle and to take advantage of this huge amount of data. In this paper, we introduce a novel approach utilizing and aggregating open datasets to compute the most-related entities for a set of weighted input entities. We optimize different algorithms for large semantic datasets enabling combining data from different semantic open sources and providing high quality results even if only limited resources are available. We evaluate our approach on a large encyclopedic dataset. The evaluation results show that our approach efficiently supports different semantic edge types. The application build on our framework provides highly relevant results and visual explanations helping the user to understand the semantic relationship between the computed entities.

Pages: 97 to 103

Copyright: Copyright (c) IARIA, 2011

Publication date: November 20, 2011

Published in: conference

ISSN: 2308-4510

ISBN: 978-1-61208-175-5

Location: Lisbon, Portugal

Dates: from November 20, 2011 to November 25, 2011