GHOST: an effective graph-based framework for name distinction

Xiaoming Fan, Jianyong Wang, Bing Lv, Lizhu Zhou, Wei Hu

Abstract: Name ambiguity stems from the fact that many people or objects share identical names. In this paper, we focus on investigating the problem in digital libraries to distinguish publications written by authors with identical names. We present an effective graph-based framework, GHOST (abbr. GrapH-based framewOrk for name diStincTion), to solve the problem systematically. We evaluated the framework on the real DBLP dataset, and the experimental results show that GHOST outperforms the state-of-the-art method.