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SUMMARY:Extracting Knowledge from the Structure of Wikipedia Links
DTSTART:20191010T110000
DTEND:20191010T120000
DTSTAMP:20260510T054614Z
UID:a22d6914b7d8dc068406cec83c78db73ab2d9a5ae6be4ad109b039eb
CATEGORIES:Conferences - Seminars
DESCRIPTION:Cristian Consonni. Cristian Consonni is a PhD student in Comp
 uter Science at the Department of Information Engineering and Computer Sci
 ence (DISI) at the University of Trento\, Italy. He is part of the dbTr
 ento group. He is interested in machine learning and data mining techniq
 ues on time-evolving graphs.\nSurfing the links between Wikipedia articles
  constitutes a valuable way to acquire new knowledge related to a topic. 
 In Wikipedia parlance\, these links are called internal links or wikilink
 s. We introduce WikiLinkGraphs: a complete\, longitudinal dataset of the 
 network of internal Wikipedia links for the 9 largest language editions. 
 The dataset contains yearly snapshots of the network and spans 17 years\,
  from the creation of Wikipedia in 2001 to March 1st\, 2018. Equipped wi
 th this data\, we explore the problem of establishing which are the most 
 relevant topics related to a given page. In Wikipedia\, the density of co
 nnections makes that\, starting from a single page\, it is possible to re
 ach virtually any other topic on the encyclopedia. A well-known option to
  solve this problem is Personalized PageRank\; its performance\, however\
 , is hindered by pages with high indegree that function as hubs and obtai
 n high scores regardless of the starting point. In this talk\, we present
  CycleRank\, a novel algorithm based on cyclic paths aimed at finding the
  most relevant nodes related to a topic. We compare the results of CycleR
 ank with those of Personalized PageRank and other algorithms derived from
  it\, both with qualitative examples and with an extensive quantitative e
 valuation. We perform different experiments based on ground truths such a
 s the number of clicks that links receive from visitors and the set of re
 lated articles highlighted by editors in the “See also” section of ea
 ch article. We find that CycleRank tends to identify pages that are more 
 relevant to the selected topic. Finally\, we show that computing CycleRan
 k is two orders of magnitude faster than computing the other baselines.
LOCATION:ELA 2 https://plan.epfl.ch/?room==ELA%202
STATUS:CONFIRMED
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