Improvement of Entity Resolution Based on Markov Logic Networks
基于马尔科夫逻辑网络的实体解析改进算法

Entity Resolution is a crucial and expensive step in the data mining process. Domingos and Singla of University of Washington proposed of well-founded, integrated solution to the entity resolution problem based on Markov Logic.This paper tried to improve Domingos and Singla's solution by adding a formula with a changeable weight to it, to handle the problem of ambiguity of entities that the original system cannot distinguish. The new algorithm can effectively handle ambiguity of entities, and improve accuracy compared with the original algorithm, which is proved by experiment s.