Author Name Disambiguation for Citations Using Topic and Web Correlation

  • Authors:

  • Kai-Hsiang Yang;Hsin-Tsung Peng;Jian-Yi Jiang;Hahn-Ming Lee;Jan-Ming Ho

  • Affiliations:

  • Institute of Information Science, Academia Sinica, Taipei, Taiwan;Institute of Information Science, Academia Sinica, Taipei, Taiwan;Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan;Institute of Information Science, Academia Sinica, Taipei, Taiwan and Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiw ...;Institute of Information Science, Academia Sinica, Taipei, Taiwan

  • Venue:
  • ECDL '08 Proceedings of the 12th European conference on Research and Advanced Technology for Digital Libraries
  • Year:
  • 2008

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Abstract

Today, bibliographic digital libraries play an important role in helping members of academic community search for novel research. In particular, author disambiguation for citations is a major problem during the data integration and cleaning process, since author names are usually very ambiguous. For solving this problem, we proposed two kinds of correlations between citations, namely, Topic Correlationand Web Correlation, to exploit relationships between citations, in order to identify whether two citations with the same author name refer to the same individual.The topic correlation measures the similarity between research topics of two citations; while the Web correlation measures the number of co-occurrence in web pages. We employ a pair-wise grouping algorithm to group citations into clusters. The results of experiments show that the disambiguation accuracy has great improvement when using topic correlation and Web correlation, and Web correlation provides stronger evidences about the authors of citations.