Author disambiguation by hierarchical agglomerative clustering with adaptive stopping criterion

Lei Cen, Eduard C. Dragut, Luo Si, Mourad Ouzzani

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

16 Citations (Scopus)

Abstract

Entity disambiguation is an important step in many information retrieval applications. This paper proposes new research for entity disambiguation with the focus of name disambiguation in digital libraries. In particular, pairwise similarity is first learned for publications that share the same author name string (ANS) and then a novel Hierarchical Agglomerative Clustering approach with Adaptive Stopping Criterion (HACASC) is proposed to adaptively cluster a set of publications that share a same ANS to individual clusters of publications with different author identities. The HACASC approach utilizes a mixture of kernel ridge regressions to intelligently determine the threshold in clustering. This obtains more appropriate clustering granularity than non-adaptive stopping criterion. We conduct a large scale empirical study with a dataset of more than 2 million publication record pairs to demonstrate the advantage of the proposed HACASC approach.

Original languageEnglish
Title of host publicationSIGIR 2013 - Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval
Pages741-744
Number of pages4
DOIs
Publication statusPublished - 2 Sep 2013
Event36th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2013 - Dublin, Ireland
Duration: 28 Jul 20131 Aug 2013

Other

Other36th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2013
CountryIreland
CityDublin
Period28/7/131/8/13

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Keywords

  • Author Disambiguation
  • Clustering

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Information Systems

Cite this

Cen, L., Dragut, E. C., Si, L., & Ouzzani, M. (2013). Author disambiguation by hierarchical agglomerative clustering with adaptive stopping criterion. In SIGIR 2013 - Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 741-744) https://doi.org/10.1145/2484028.2484157