Inproceedings3510: Unterschied zwischen den Versionen
Xi2839 (Diskussion | Beiträge) |
Xi2839 (Diskussion | Beiträge) |
||
Zeile 13: | Zeile 13: | ||
|Month=October | |Month=October | ||
|Booktitle=The Semantic Web: ESWC 2016 Satellite Events, Heraklion, Crete, Greece, May 29 - June 2, 2016, Revised Selected Papers | |Booktitle=The Semantic Web: ESWC 2016 Satellite Events, Heraklion, Crete, Greece, May 29 - June 2, 2016, Revised Selected Papers | ||
− | |Publisher=Springer | + | |Publisher=Springer International Publishing |
|Note= | |Note= | ||
}} | }} | ||
Zeile 20: | Zeile 20: | ||
}} | }} | ||
{{Publikation Details | {{Publikation Details | ||
− | |Abstract=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. | + | |Abstract=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, 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 their position in the article text - can be deemed more important than others. In our variants, we change the probabilistic impact of links in accordance to their position on the page and measure the effects on the output of the PageRank algorithm. We 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. | + | 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 their context/position in the article text-can be deemed more important than others. In our variants, we 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. We 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. |
|Download=Wikipedia pagerank.pdf, | |Download=Wikipedia pagerank.pdf, | ||
|Projekt=SumOn, XLiMe | |Projekt=SumOn, XLiMe |
Version vom 25. Oktober 2016, 15:14 Uhr
PageRank on Wikipedia: Towards General Importance Scores for Entities
PageRank on Wikipedia: Towards General Importance Scores for Entities
Published: 2016
October„October“ befindet sich nicht in der Liste (Januar, Februar, März, April, Mai, Juni, Juli, August, September, Oktober, ...) zulässiger Werte für das Attribut „Month“.
Buchtitel: The Semantic Web: ESWC 2016 Satellite Events, Heraklion, Crete, Greece, May 29 - June 2, 2016, Revised Selected Papers
Verlag: Springer International Publishing
Referierte Veröffentlichung
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 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 their context/position in the article text-can be deemed more important than others. In our variants, we 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. We 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.
Download: Media:Wikipedia pagerank.pdf
Web Science und Wissensmanagement
Vernetzte Daten, Information Retrieval, Semantische Suche, Entitätszusammenfassung, Semantic Web