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ReConRank: A Scalable Ranking Method for Semantic Web Data with Context

ARAN - Access to Research at NUI Galway

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dc.contributor.author Hogan, Aidan en
dc.contributor.author Harth, Andreas en
dc.contributor.author Decker, Stefan en
dc.date.accessioned 2009-12-10T14:37:42Z en
dc.date.available 2009-12-10T14:37:42Z en
dc.date.issued 2006 en
dc.identifier.citation Aidan Hogan, Andreas Harth, Stefan Decker "ReConRank: A Scalable Ranking Method for Semantic Web Data with Context", Proceedings of Second International Workshop on Scalable Semantic Web Knowledge Base Systems (SSWS 2006), in conjunction with International Semantic Web Conference (ISWC 2006), 2006. en
dc.identifier.uri http://hdl.handle.net/10379/492 en
dc.description.abstract We present an approach that adapts the well-known PageRank/HITS algorithms to Semantic Web data. Our method combines ranks from the RDF graph with ranks from the context graph, i.e. data sources and their linkage. We present performance evaluation results based on a large RDF data set obtained from the Web. en
dc.format application/pdf en
dc.language.iso en en
dc.subject.lcsh Semantic Web en
dc.subject.lcsh RDF (Document markup language) en
dc.title ReConRank: A Scalable Ranking Method for Semantic Web Data with Context en
dc.type Workshop paper en
dc.description.peer-reviewed peer-reviewed en
dc.contributor.funder Science Foundation Ireland en

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