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Random Local SVMs for Classifying Large Datasets Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Finding Similar Artists from the Web of Data: A PageRank Based Semantic Similarity Metric

Authors : Phuong T. Nguyen, Hong Anh Le

Published in: Future Data and Security Engineering

Publisher: Springer International Publishing

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Abstract

Since its commencement, the Linked Open Data cloud has been quickly become popular and offers rich data sources for quite a number of applications. The potential for application development using Linked Data is immense and needs intensive research efforts. Until now, the issue of how to efficiently exploit the data provided by the new platform remains an open research question. In this paper we present our investigation of utilizing a well-known encyclopedic dataset, DBpedia for finding similar musical artists. Our approach exploits a PageRank based semantic similarity metric for computing similarity in RDF graph. From the data provided by DBpedia, the similarity results help find out similar artists for a given artist. By doing this, we are also be able to examine the suitability of DBpedia for this type of recommendation tasks. Experimental results show that the outcomes are encouraging.

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Footnotes
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Metadata
Title
Finding Similar Artists from the Web of Data: A PageRank Based Semantic Similarity Metric
Authors
Phuong T. Nguyen
Hong Anh Le
Copyright Year
2015
Book
Future Data and Security Engineering

Print ISBN: 978-3-319-26134-8

Electronic ISBN: 978-3-319-26135-5

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-26135-5_8

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