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.
| Original language | English |
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| Title of host publication | SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval |
| Pages | 1233-1234 |
| Number of pages | 2 |
| DOIs | |
| Publication status | Published - 1 Sept 2011 |
| Event | 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'11 - Beijing, China Duration: 24 Jul 2011 → 28 Jul 2011 |
Conference
| Conference | 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'11 |
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| Country/Territory | China |
| City | Beijing |
| Period | 24/07/11 → 28/07/11 |
Keywords
- Bipartite graph
- Person name disambiguation
- Social network
ASJC Scopus subject areas
- Information Systems