Hybrid entity clustering using crowds and data

Hybrid entity clustering using crowds and data Query result clustering has attracted considerable attention as a means of providing users with a concise overview of results. However, little research effort has been devoted to organizing the query results for entities which refer to real-world concepts, e.g., people, products, and locations. Entity-level result clustering is more challenging because diverse similarity notions between entities need to be supported in heterogeneous domains, e.g., image resolution is an important feature for cameras, but not for fruits. To address this challenge, we propose a hybrid relationship clustering algorithm, called Hydra , using co-occurrence and numeric features . Algorithm Hydra captures diverse user perceptions from co-occurrence and disambiguates different senses using feature-based similarity. In addition, we extend Hydra into $${\mathsf{Hydra }_\mathsf{gData }}$$ Hydra gData with different sources, i.e., entity types and crowdsourcing . Experimental results show that the proposed algorithms achieve effectiveness and efficiency in real-life and synthetic datasets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Hybrid entity clustering using crowds and data

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2013 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-013-0328-8
Publisher site
See Article on Publisher Site

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