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

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.rights	Attribution-NonCommercial-NoDerivs 3.0 Ireland
dc.rights.uri	https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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
nui.item.downloads	1985

Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Ireland