A Comparison of Techniques for Name Matching

Taoxin Peng ., Lin Li ., Jessie Kennedy .


Information explosion is a problem for everyone nowadays. It is a great challenge to all kinds of businesses to maintain high quality of data in their information applications, such as data integration, text and web mining, information retrieval, search engine, etc. In such applications, matching names is one of the popular tasks. There are a number of name matching techniques available. Unfortunately, there is no existing name matching technique that performs the best in all situations. Therefore, a problem that every researcher or a practitioner has to face is how to select an appropriate technique for a given dataset. This paper analyses and evaluates a set of popular name matching techniques on several carefully designed different datasets. The experimental comparison confirms the statement that there is no clear best technique. Some suggestions have been presented, which can be used as guidance for researchers and practitioners to select an appropriate name matching technique in a given dataset.

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