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

Hogan, Aidan
; 
Harth, Andreas
; 
Decker, Stefan
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Identifiers
http://hdl.handle.net/10379/492
 https://doi.org/10.13025/21396
Publication Date
2006
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Type
Workshop paper
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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.
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.
Funder
Science Foundation Ireland
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Attribution-NonCommercial-NoDerivs 3.0 Ireland
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Data Science Institute (Workshop Papers)