@article{
 title = {A Bipartite Graph Based Social Network Splicing Method for Person Name Disambiguation},
 type = {article},
 year = {2011},
 identifiers = {[object Object]},
 keywords = {bipartite graph,person name disambiguation,social network},
 pages = {1233-1234},
 volume = {2},
 websites = {http://dl.acm.org/citation.cfm?id=2009916.2010135},
 publisher = {ACM Press},
 id = {87486316-3c3b-3d16-af45-177d56620ced},
 created = {2012-02-28T00:51:15.000Z},
 file_attached = {false},
 profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},
 group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},
 last_modified = {2017-03-14T14:36:19.698Z},
 tags = {personal name disambiguation},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Tang2011},
 private_publication = {false},
 abstract = {The key issue of person name disambiguation is to discover different namesakes in massive web documents rather than simply cluster documents by using textual features. In this paper, we describe a novel person name disambiguation method based on social networks to effectively identify namesakes. The social network snippets in each document are extracted. Then, the namesakes are identified via splicing the social networks of each namesake by using the snippets as a bipartite graph. Experimental results show that our method achieves better result than the top performance of WePS-2 in identifying different namesakes.},
 bibtype = {article},
 author = {Tang, Jintao and Lu, Qin and Wang, Ting and Wang, Ji and Li, Wenjie},
 journal = {Evaluation},
 number = {1}
}

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