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.

Finding similar artists from the web of data: A pagerank based semantic similarity metric

Nguyen Phuong Thanh
Writing – Review & Editing
;
2015

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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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11697/183216
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