Difference between revisions of "Pagerank on Wikipedia: Towards General Importance Scores for Entities"

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== Overview ==
 
== Overview ==
 
Link analysis methods are used to estimate importance in graph-structured data. In that realm, the PageRank algorithm has been used to analyze directed graphs, in particular the link structure of the Web. Recent developments in [[information retrieval]] focus on entities and their relations (i.e., knowledge graph panels). Many entities are documented in the popular knowledge base [[Wikipedia]]. The cross-references within Wikipedia exhibit a directed graph structure that is suitable for computing PageRank scores as importance [[indicators]] for entities. In this work, authors present different PageRank-based analyses on the link graph of Wikipedia and according experiments. Authors focus on the question whether some links—based on their context/position in the article text—can be deemed more important than others. In variants, authors change the probabilistic impact of links in accordance to their context/position on the page and measure the effects on the output of the PageRank algorithm. Authors compare the resulting rankings and those of existing systems with page-view-based rankings and provide statistics on the pairwise computed Spearman and Kendall rank correlations.
 
Link analysis methods are used to estimate importance in graph-structured data. In that realm, the PageRank algorithm has been used to analyze directed graphs, in particular the link structure of the Web. Recent developments in [[information retrieval]] focus on entities and their relations (i.e., knowledge graph panels). Many entities are documented in the popular knowledge base [[Wikipedia]]. The cross-references within Wikipedia exhibit a directed graph structure that is suitable for computing PageRank scores as importance [[indicators]] for entities. In this work, authors present different PageRank-based analyses on the link graph of Wikipedia and according experiments. Authors focus on the question whether some links—based on their context/position in the article text—can be deemed more important than others. In variants, authors change the probabilistic impact of links in accordance to their context/position on the page and measure the effects on the output of the PageRank algorithm. Authors compare the resulting rankings and those of existing systems with page-view-based rankings and provide statistics on the pairwise computed Spearman and Kendall rank correlations.
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=== Wikipedia Quality ===
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Thalhammer, Andreas; Rettinger, Achim. (2016). "[[Pagerank on Wikipedia: Towards General Importance Scores for Entities]]". Springer, Cham. DOI: 10.1007/978-3-319-47602-5_41.
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=== English Wikipedia ===
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{{cite journal |last1=Thalhammer |first1=Andreas |last2=Rettinger |first2=Achim |title=Pagerank on Wikipedia: Towards General Importance Scores for Entities |date=2016 |doi=10.1007/978-3-319-47602-5_41 |url=https://wikipediaquality.com/wiki/Pagerank_on_Wikipedia:_Towards_General_Importance_Scores_for_Entities |journal=Springer, Cham}}
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=== HTML ===
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Thalhammer, Andreas; Rettinger, Achim. (2016). &amp;quot;<a href="https://wikipediaquality.com/wiki/Pagerank_on_Wikipedia:_Towards_General_Importance_Scores_for_Entities">Pagerank on Wikipedia: Towards General Importance Scores for Entities</a>&amp;quot;. Springer, Cham. DOI: 10.1007/978-3-319-47602-5_41.
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Revision as of 17:24, 6 October 2020


Pagerank on Wikipedia: Towards General Importance Scores for Entities
Authors
Andreas Thalhammer
Achim Rettinger
Publication date
2016
DOI
10.1007/978-3-319-47602-5_41
Links
Original

Pagerank on Wikipedia: Towards General Importance Scores for Entities - scientific work related to Wikipedia quality published in 2016, written by Andreas Thalhammer and Achim Rettinger.

Overview

Link analysis methods are used to estimate importance in graph-structured data. In that realm, the PageRank algorithm has been used to analyze directed graphs, in particular the link structure of the Web. Recent developments in information retrieval focus on entities and their relations (i.e., knowledge graph panels). Many entities are documented in the popular knowledge base Wikipedia. The cross-references within Wikipedia exhibit a directed graph structure that is suitable for computing PageRank scores as importance indicators for entities. In this work, authors present different PageRank-based analyses on the link graph of Wikipedia and according experiments. Authors focus on the question whether some links—based on their context/position in the article text—can be deemed more important than others. In variants, authors change the probabilistic impact of links in accordance to their context/position on the page and measure the effects on the output of the PageRank algorithm. Authors compare the resulting rankings and those of existing systems with page-view-based rankings and provide statistics on the pairwise computed Spearman and Kendall rank correlations.

Embed

Wikipedia Quality

Thalhammer, Andreas; Rettinger, Achim. (2016). "[[Pagerank on Wikipedia: Towards General Importance Scores for Entities]]". Springer, Cham. DOI: 10.1007/978-3-319-47602-5_41.

English Wikipedia

{{cite journal |last1=Thalhammer |first1=Andreas |last2=Rettinger |first2=Achim |title=Pagerank on Wikipedia: Towards General Importance Scores for Entities |date=2016 |doi=10.1007/978-3-319-47602-5_41 |url=https://wikipediaquality.com/wiki/Pagerank_on_Wikipedia:_Towards_General_Importance_Scores_for_Entities |journal=Springer, Cham}}

HTML

Thalhammer, Andreas; Rettinger, Achim. (2016). &quot;<a href="https://wikipediaquality.com/wiki/Pagerank_on_Wikipedia:_Towards_General_Importance_Scores_for_Entities">Pagerank on Wikipedia: Towards General Importance Scores for Entities</a>&quot;. Springer, Cham. DOI: 10.1007/978-3-319-47602-5_41.