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In this work, we present different PageRank-based analyses on the link graph of Wikipedia and according experiments. We focus on the question whether some links - based on the location of occurrence - can be deemed more important than others. In our variants, we change the probabilistic impact of links in accordance to their location on the page and measure the effects on the output of the PageRank algorithm. We compare these variants and the rankings of existing systems with page-view-based rankings and provide statistics on the pairwise computed Spearman and Kendall rank correlations.
 
In this work, we present different PageRank-based analyses on the link graph of Wikipedia and according experiments. We focus on the question whether some links - based on the location of occurrence - can be deemed more important than others. In our variants, we change the probabilistic impact of links in accordance to their location on the page and measure the effects on the output of the PageRank algorithm. We compare these variants and the rankings of existing systems with page-view-based rankings and provide statistics on the pairwise computed Spearman and Kendall rank correlations.
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|Projekt=SumOn, XLiMe
 
|Forschungsgruppe=Web Science und Wissensmanagement
 
|Forschungsgruppe=Web Science und Wissensmanagement
 
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Version vom 5. April 2016, 11:29 Uhr


PageRank on Wikipedia: Towards General Importance Scores for Entities


PageRank on Wikipedia: Towards General Importance Scores for Entities



Published: 2016 Mai

Buchtitel: Know∂LOD 2016
Verlag: CEUR-WS

Referierte VeröffentlichungNote: to appear

BibTeX


Kurzfassung
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 many entities.

In this work, we present different PageRank-based analyses on the link graph of Wikipedia and according experiments. We focus on the question whether some links - based on the location of occurrence - can be deemed more important than others. In our variants, we change the probabilistic impact of links in accordance to their location on the page and measure the effects on the output of the PageRank algorithm. We compare these variants and the rankings of existing systems with page-view-based rankings and provide statistics on the pairwise computed Spearman and Kendall rank correlations.


Projekt

SumOnXLiMe


Verknüpfte Datasets

DBpedia PageRank


Forschungsgruppe

Web Science und Wissensmanagement


Forschungsgebiet

Vernetzte Daten, Information Retrieval, Semantische Suche, Entitätszusammenfassung, Semantic Web