Matching person names through name transformation

Authors:
Jun Gong;Lidan Wang;Douglas W. Oard
Affiliations:
Beihang University, Beijing, China;University of Maryland, College Park, MD, USA;University of Maryland, College Park, MD, USA
Venue:
Proceedings of the 18th ACM conference on Information and knowledge management
Year:
2009

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Abstract

Matching person names plays an important role in many applications, including bibliographic databases and indexing systems. Name variations and spelling errors make exact string matching problematic; therefore, it is useful to develop methodologies that can handle variant forms for the same named entity. In this paper, a novel person name matching model is presented. Common name variations in the English speaking world are formalized, and the concept of name transformation paths is introduced; name similarity is measured after the best transformation path has been selected. Supervised techniques are used to learn a similarity function and a decision rule. Experiments with three datasets show the method to be effective.