Abstract
Entity resolution (ER) is the problem of identifying and grouping different manifestations of the same real world object. Algorithmic approaches have been developed where most tasks offer superior performance under supervised learning. However, the prohibitive cost of labeling training data is still a huge obstacle for detecting duplicate query records from online sources. Furthermore, the unique combinations of noisy data with missing elements make ER tasks more challenging. To address this, transfer learning has been adopted to adaptively share learned common structures of similarity scoring problems between multiple sources. Although such techniques reduce the labeling cost so that it is linear with respect to the number of sources, its random sampling strategy is not successful enough to handle the ordinary sample imbalance problem. In this paper, we present a novel multi-source active transfer learning framework to jointly select fewer data instances from all sources to train classifiers with constant precision/recall. The intuition behind our approach is to actively label the most informative samples while adaptively transferring collective knowledge between sources. In this way, the classifiers that are learned can be both label-economical and flexible even for imbalanced or quality diverse sources. We compare our method with the state-of-the-art approaches on real-word datasets. Our experimental results demonstrate that our active transfer learning algorithm can achieve impressive performance with far fewer labeled samples for record matching with numerous and varied sources.
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Jie Xin received her MS in computer science from Queen Mary University of London, England in 2006. She is currently a PHD candidate at Soochow University. Her research interests include information retrieval and integration, and data mining.
Zhiming Cui is a professor and doctoral supervisor at the Institute of Computer Science and Technology, Soochow University, China and a CCF senior member. His researches areas are intelligent information processing, image processing, distributing computing, deep Web data mining, etc.
Pengpeng Zhao is an associate professor in the Department of Computer Science and Technology at Soochow University, China. He received his PhD in computer science from Soochow University in 2008. His main research interests are in the study of the management, retrieval, and mining of information on the World-Wide Web.
Tianxu He received his MS in computer science from Soochow University, China in 2006. He is a PhD candidate at Soochow University. His research interests include data mining and knowledge discovery in databases.
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Xin, J., Cui, Z., Zhao, P. et al. Active transfer learning of matching query results across multiple sources. Front. Comput. Sci. 9, 595–607 (2015). https://doi.org/10.1007/s11704-015-4068-3
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DOI: https://doi.org/10.1007/s11704-015-4068-3