ZenCrowd: Leveraging probabilistic reasoning and crowdsourcing techniques for large-scale entity linking

Gianluca Demartini, Djellel Eddine Difallah, Philippe Cudré-Mauroux

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We tackle the problem of entity linking for large collections of online pages; Our system, ZenCrowd, identifies entities from natural language text using state of the art techniques and automatically connects them to the Linked Open Data cloud. We show how one can take advantage of human intelligence to improve the quality of the links by dynamically generating micro-tasks on an online crowdsourcing platform. We develop a probabilistic framework to make sensible decisions about candidate links and to identify unreliable human workers. We evaluate ZenCrowd in a real deployment and show how a combination of both probabilistic reasoning and crowdsourcing techniques can significantly improve the quality of the links, while limiting the amount of work performed by the crowd.

Original languageEnglish (US)
Title of host publicationWWW'12 - Proceedings of the 21st Annual Conference on World Wide Web
Pages469-478
Number of pages10
DOIs
StatePublished - 2012
Event21st Annual Conference on World Wide Web, WWW'12 - Lyon, France
Duration: Apr 16 2012Apr 20 2012

Publication series

NameWWW'12 - Proceedings of the 21st Annual Conference on World Wide Web

Conference

Conference21st Annual Conference on World Wide Web, WWW'12
CountryFrance
CityLyon
Period4/16/124/20/12

Keywords

  • Crowdsourcing
  • Entity linking
  • Linked data
  • Probabilistic reasoning

ASJC Scopus subject areas

  • Computer Networks and Communications

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