Please use this identifier to cite or link to this item:
|Title:||Adaptive sorted neighborhood methods for efficient record linkage|
|Citation:||Yan, S., Lee, D., Kan, M.-Y., Giles, L.C. (2007). Adaptive sorted neighborhood methods for efficient record linkage. Proceedings of the ACM International Conference on Digital Libraries : 185-194. ScholarBank@NUS Repository. https://doi.org/10.1145/1255175.1255213|
|Abstract:||Traditionally, record linkage algorithms have played an important role in maintaining digital libraries - i.e., identifying matching citations or authors for consolidation in updating or integrating digital libraries. As such, a variety of record linkage algorithms have been developed and deployed successfully. Often, however, existing solutions have a set of parameters whose values are set by human experts off-lineand are fixed during the execution. Since finding the ideal values of such parameters is not straightforward, or no such single ideal value even exists, the applicability of existing solutions to new scenarios or domains is greatly hampered. To remedy this problem, we argue that one can achieve significant improvement by adaptively and dynamically changing such parameters of record linkage algorithms. To validate our hypothesis, we take a classical record linkage algorithm, the sorted neighborhood method (SNM), and demonstrate how we can achieve improved accuracy and performance by adaptively changing its fixed sliding window size. Our claim is analytically and empirically validated using both real and synthetic data sets of digital libraries and other domains. Copyright 2007 ACM.|
|Source Title:||Proceedings of the ACM International Conference on Digital Libraries|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Mar 23, 2019
WEB OF SCIENCETM
checked on Mar 13, 2019
checked on Mar 24, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.