关键词: 字符串相似度, 名字消歧, 名字匹配, 机器学习

Abstract: Entity name matching plays an important role in information system integration applications, while the name variations and clerical errors in Chinese entity names make exact string matching problematic. Therefore it is important to develop methodologies that can handle the different variants of the same name entity. The Chinese entity name similarity is measured based on character, word and semantic levels separately, and a hybrid solution is introduced by combining these similarities linearly. Two machine learning methods are developed to integrate editing features for more precise matching: the optimized ranking list and best cut point are achieved from a training process; a Support Vector Machine is used to judge the name pairs. The results of an experimental study on a real dataset of Chinese entity names are reported; the experiment results show the methods are effective.

Key words: string similarity, name disambiguation, name-matching, machine learning