Bootstrapping active name disambiguation with crowdsourcing

Yu Cheng, Zhengzhang Chen, Jiang Wang, Ankit Agrawal, Alok Nidhi Choudhary

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

6 Scopus citations


Name disambiguation is a challenging and important problem in many domains, such as digital libraries, social media management and people search systems. Traditional methods, based on direct assignment using supervised machine learning techniques, seem to be the most effective, but their performances are highly dependent on the amount of training data, while large data annotation can be expensive and time-consuming requiring hours of manual inspection by a domain expert. To efficiently acquire labeled data, we propose a bootstrapping algorithm for the name disambiguation task based on active learning and crowdsourced labeling. We show that the proposed method can leverage the advantages of exploration and exploitation by combining two strategies, thereby improving the overall quality of the training data at minimal expense. The experimental results on two datasets DBLP and ArnetMiner demonstrate the superiority of our framework over existing methods.

Original languageEnglish (US)
Title of host publicationCIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management
Number of pages4
StatePublished - Dec 11 2013
Event22nd ACM International Conference on Information and Knowledge Management, CIKM 2013 - San Francisco, CA, United States
Duration: Oct 27 2013Nov 1 2013


Other22nd ACM International Conference on Information and Knowledge Management, CIKM 2013
Country/TerritoryUnited States
CitySan Francisco, CA


  • Active learning
  • Bootstrapping
  • Crowdsourcing
  • Name disambiguation

ASJC Scopus subject areas

  • General Business, Management and Accounting
  • General Decision Sciences


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