Person name disambiguation of searching results using social network

The person names are so ambiguous that the results for searching a person name are usually a mixture of pages about the namesakes. This paper presents a novel approach leveraging the fact that each namesake has a unique social community. Firstly, the social network of the person name to search is found and extended by employing the co-occurrence of person names in snippets returned by a search engine, then automatically clustered into different social communities by the algorithm combining spectral partition and modularity evaluation. Finally, the search results are clustered into different groups where each contains pages referring to the same individual. On the corpus of Chinese person names, experimental results show that the whole performance achieves high level and graph clustering algorithm benefits improving disambiguation effect from further dividing the connecting social network.