GHOST: an effective graph-based framework for name distinction

ABSTRACT

Name ambiguity stems from the fact that many people or objects share identical names. In this paper, we focus on investigating the problem in digital libraries to distinguish publications written by authors with identical names. We present an effective graph-based framework, GHOST (abbr. GrapH-based framewOrk for name diStincTion), to solve the problem systematically. We evaluated the framework on the real DBLP dataset, and the experimental results show that GHOST outperforms the state-of-the-art method.

REFERENCES

• 1: B. J. Frey and D. Dueck. Clustering by passing messages between data points. Science, 2007.
• 2: Hui Han , Lee Giles , Hongyuan Zha , Cheng Li , Kostas Tsioutsiouliklis, Two supervised learning approaches for name disambiguation in author citations, Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries, June 07-11, 2004, Tuscon, AZ, USA [doi>10.1145/996350.996419]
• 3: X. Yin, J. Han, and P. S. Yu. Object distinction: Distinguishing objects with identical names. In ICDE, 2007.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications

Additional Classification:
  E. Data
  E.1 DATA STRUCTURES
      Subjects: Graphs and networks

General Terms:
Algorithms, Experimentation

Keywords:
clustering, graph, name distinction, similarity