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Efficient Name Disambiguation in Digital Libraries

  • Jia Zhu
  • Gabriel Fung
  • Liwei Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6897)

Abstract

In digital libraries, ambiguous author names occur due to the existence of multiple authors with the same name or different name variations for the same person. Most of the previous works to solve this issue also known as name disambiguation often employ hierarchal clustering approaches based on information inside the citation records, e.g. co-authors and publication titles. In this paper, we propose an approach that can effectively identify and retrieve information from web pages and use the information to disambiguate authors. Initially, we implement a web pages identification model by using a neural network classifier and traffic rank. Considering those records can not be found directly in personal pages, we then enhance the model to handle such case during the clustering process with performance improvement. We examine our approach on a subset of digital library records and the result is reasonable effective.

Keywords

Search Engine Neural Network Model Digital Library Latent Dirichlet Allocation Probabilistic Latent Semantic Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jia Zhu
    • 1
  • Gabriel Fung
    • 2
  • Liwei Wang
    • 3
  1. 1.School of ITEEThe University of QueenslandAustralia
  2. 2.iConcept PressHong Kong
  3. 3.Wuhan UniversityChina

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