Journal of the ACM (JACM)
Two supervised learning approaches for name disambiguation in author citations
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Disambiguating Web appearances of people in a social network
WWW '05 Proceedings of the 14th international conference on World Wide Web
Comparative study of name disambiguation problem using a scalable blocking-based framework
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Reference reconciliation in complex information spaces
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Exploiting the hierarchical structure for link analysis
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Unsupervised personal name disambiguation
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Named entity disambiguation by leveraging wikipedia semantic knowledge
Proceedings of the 18th ACM conference on Information and knowledge management
Web personal name disambiguation based on reference entity tables mined from the web
Proceedings of the eleventh international workshop on Web information and data management
Structural semantic relatedness: a knowledge-based method to named entity disambiguation
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
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Searching for information about a particular person is a common activity on search engines. However, current search engines do not provide any special function for search a person. Previous research has solved the problem by using additional background knowledge, such as a friend list, to cluster the searched web pages. However, it is still difficult to retrieve and choose suitable background knowledge. In this paper, we propose a Web Appearance Disambiguation (WAD) system to solve the problem by only using the hyperlink structures between web pages. The key idea of the WAD system is to find out smaller node motifs as evidences of close relationship between pages for clustering searched web pages. Our experimental results show that, under no background knowledge, the performance of the WAD system achieves 70% for the F-measure.