Disambiguating Authors by Pairwise Classification |
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Authors: | Quan Lin Ì Bo Wang Î Yuan Du Xuezhi Wang ê Yuhua Li Songcan Chen |
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Institution: | a Department of Computer Science, Huazhong University of Science and Technology, Wuhan 430074, China;b Department of Computer Science, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;c Department of Computer Science, Tsinghua University, Beijing 100084, China |
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Abstract: | Name ambiguity is a critical problem in many applications, in particular in online bibliography systems, such as DBLP, ACM, and CiteSeerx. Despite the many studies, this problem is still not resolved and is becoming even more serious, especially with the increasing popularity of Web 2.0. This paper addresses the problem in the academic researcher social network ArnetMiner using a supervised method for exploiting all side information including co-author, organization, paper citation, title similarity, author's homepage, web constraint, and user feedback. The method automatically determines the person number k. Tests on the researcher social network with up to 100 different names show that the method significantly outperforms the baseline method using an unsupervised attribute-augmented graph clustering algorithm. |
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Keywords: | disambiguating pairwise classification arnetminer |
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