Joint entity resolution on multiple datasets

Joint entity resolution on multiple datasets Entity resolution (ER) is the problem of identifying which records in a database represent the same entity. Often, records of different types are involved (e.g., authors, publications, institutions, venues), and resolving records of one type can impact the resolution of other types of records. In this paper we propose a flexible, modular resolution framework where existing ER algorithms developed for a given record type can be plugged in and used in concert with other ER algorithms. Our approach also makes it possible to run ER on subsets of similar records at a time, important when the full data are too large to resolve together. We study the scheduling and coordination of the individual ER algorithms, in order to resolve the full dataset, and show the scalability of our approach. We also introduce a “state-based” training technique where each ER algorithm is trained for the particular execution context (relative to other types of records) where it will be used. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Joint entity resolution on multiple datasets

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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/s00778-013-0308-z
Publisher site
See Article on Publisher Site

Abstract

Entity resolution (ER) is the problem of identifying which records in a database represent the same entity. Often, records of different types are involved (e.g., authors, publications, institutions, venues), and resolving records of one type can impact the resolution of other types of records. In this paper we propose a flexible, modular resolution framework where existing ER algorithms developed for a given record type can be plugged in and used in concert with other ER algorithms. Our approach also makes it possible to run ER on subsets of similar records at a time, important when the full data are too large to resolve together. We study the scheduling and coordination of the individual ER algorithms, in order to resolve the full dataset, and show the scalability of our approach. We also introduce a “state-based” training technique where each ER algorithm is trained for the particular execution context (relative to other types of records) where it will be used.

Journal

The VLDB JournalSpringer Journals

Published: Dec 1, 2013

References

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