A hybrid cross-language name matching technique using novel modified Levenshtein Distance

Abstract

Name matching is a key component in various applications in our life like record linkage and data mining applications. This process suffers from multiple complexities such as matching data from different languages or data written by people from different cultures. In this paper, we present a new modified Cross-Language Levenshtein Distance (CLLD) algorithm that supports matching names across different writing scripts and with many-to-many characters mapping. In addition, we present a hybrid cross-language name matching technique that uses phonetic matching technique mixed with our proposed CLLD algorithm to improve the overall f-measure and speed up the matching process. Our experiments demonstrate that this method substantially outperforms a number of well-known standard phonetic and approximate string similarity methods in terms of precision, recall, and f-measure.