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Entity Resolution with Weighted Constraints

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Book cover Advances in Databases and Information Systems (ADBIS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8716))

Abstract

Constraints ubiquitously exist in many real-life applications for entity resolution (ER). However, it is always challenging to effectively specify and efficiently use constraints when performing ER tasks. In particular, not every constraint is equally effective or robust, and using weights to express the “confidences” on constraints becomes a natural choice. In this paper, we study entity resolution (ER) (i.e., the problem of determining which records in a database refer to the same entities) in the presence of weighted constraints. We propose a unified framework that can interweave positive and negative constraints into the ER process, and investigate how effectively and efficiently weighted constraints can be used for generating ER clustering results. Our experimental study shows that using weighted constraints can lead to improved ER quality and scalability.

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Author information

Authors and Affiliations

  1. Research School of Computer Science, The Australian National University, Australia

    Zeyu Shen & Qing Wang

Authors
  1. Zeyu Shen
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  2. Qing Wang
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Editor information

Editors and Affiliations

  1. Department of Informatics, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece

    Yannis Manolopoulos

  2. EECS Department, Northwestern University, 2145 Sheridan Road, 60208, Evanston, IL, USA

    Goce Trajcevski

  3. Faculty of Computer Sciences and Engineering, University Ss. Cyril and Methodius Skopje, Rugjer Boshkovikj 16, 1000, Skopje, Macedonia

    Margita Kon-Popovska

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© 2014 Springer International Publishing Switzerland

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Shen, Z., Wang, Q. (2014). Entity Resolution with Weighted Constraints. In: Manolopoulos, Y., Trajcevski, G., Kon-Popovska, M. (eds) Advances in Databases and Information Systems. ADBIS 2014. Lecture Notes in Computer Science, vol 8716. Springer, Cham. https://doi.org/10.1007/978-3-319-10933-6_23

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  • DOI: https://doi.org/10.1007/978-3-319-10933-6_23

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10932-9

  • Online ISBN: 978-3-319-10933-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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