a unified framework for name disambiguation
Name ambiguity problem has been a challenging issue for a long history. In this paper, we intend to make a thorough investigation of the whole problem. Specifically, we formalize the name disambiguation problem in a unified framework. The framework can incorporate both attribute and relationship into a probabilistic model. We explore a dynamic approach for automatically estimating the person number K and employ an adaptive distance measure to estimate the distance between objects. Experimental results show that our proposed framework can significantly outperform the baseline method.
name disambiguation probabilistic model digital library