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

Aidan Hogan; Andreas Harth; Stefan Decker

doi:10.13025/21396

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

Aidan Hogan
, Andreas Harth
, Stefan Decker

Computer Science

Research output: Contribution to conference (Published) › Paper

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.

Original language	English (Ireland)
DOIs	https://doi.org/10.13025/21396
Publication status	Published - 1 Jan 2006

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10.13025/21396Licence: CC BY-NC-ND

PaperAccepted author manuscript, 466 KBLicence: CC BY-NC-ND

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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.",

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AU - Harth, Andreas

AU - Decker, Stefan

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AB - 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.

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DO - 10.13025/21396

M3 - Paper

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

Abstract

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