PageRank on Wikipedia: Towards General Importance Scores for Entities
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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.
KeywordsWikipedia DBpedia PageRank Link analysis Page views Rank correlation
The authors would like to thank Thimo Britsch for his contributions on the first versions of the SiteLinkExtractor tool. They also would like to thank Paul Houle and Sebastiano Vigna for their pointers and insights. The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 611346 and by the German Federal Ministry of Education and Research (BMBF) within the Software Campus project “SumOn” (grant no. 01IS12051).
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