GRAPE: a system for disambiguating and tagging people names in web search
Proceedings of the 19th international conference on World wide web
On Graph-Based Name Disambiguation
Journal of Data and Information Quality (JDIQ)
Exploiting Web querying for Web people search
ACM Transactions on Database Systems (TODS)
LINDEN: linking named entities with knowledge base via semantic knowledge
Proceedings of the 21st international conference on World Wide Web
Explore person specific evidence in web person name disambiguation
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
CWePS: chinese web people search
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
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Finding information about people using search engines is one of the most common activities on the Web. However, search engines usually return a long list of Web pages, which may be relevant to many namesakes, especially given the explosive growth of Web data. To address the challenge caused by name ambiguity in Web people search, this paper proposes a novel graph-based framework, GRAPE (abbr. a Graph-based fRamework for disAmbiguating People appEarances in Web search). In GRAPE, people tag information (e.g., people name, organization, and email address) surrounding the queried people name is extracted from the search results, a graph-based unsupervised algorithm is then developed to cluster the extracted tags, where a new method, Cohesion, is introduced to measure the importance of a tag for clustering, and each final cluster of tags represents a unique people entity. Experimental results show that our proposed framework outperforms the state-of-the-art Web people name disambiguation approaches.