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Adaptive Temporal Entity Resolution on Dynamic Databases

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Advances in Knowledge Discovery and Data Mining (PAKDD 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7819))

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Abstract

Entity resolution is the process of matching records that refer to the same entities from one or several databases in situations where the records to be matched do not include unique entity identifiers. Matching therefore has to rely upon partially identifying information, such as names and addresses. Traditionally, entity resolution has been applied in batch-mode and on static databases. However, increasingly organisations are challenged by the task of having a stream of query records that need to be matched to a database of known entities. As these query records are matched, they are inserted into the database as either representing a new entity, or as the latest embodiment of an existing entity. We investigate how temporal and dynamic aspects, such as time differences between query and database records and changes in database content, affect matching quality. We propose an approach that adaptively adjusts similarities between records depending upon the values of the records’ attributes and the time differences between records. We evaluate our approach on synthetic data and a large real US voter database, with results showing that our approach can outperform static matching approaches.

This research was funded by the Australian Research Council (ARC), Veda Advantage, and Funnelback Pty. Ltd., under Linkage Project LP100200079.

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Author information

Authors and Affiliations

  1. Research School of Computer Science, The Australian National University, Canberra, ACT, 0200, Australia

    Peter Christen

  2. Veda, Melbourne, VIC, 3000, Australia

    Ross W. Gayler

Authors
  1. Peter Christen
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  2. Ross W. Gayler
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Editor information

Editors and Affiliations

  1. School of Computing Science, Simon Fraser University, 8888 University Drive, V5A 1S6, Burnaby, BC, Canada

    Jian Pei

  2. Dept. of Computer Science and Information Engineering, Institute of Medical Informatics, National Cheng Kung University, Tainan, Taiwan

    Vincent S. Tseng

  3. Faculty of Engineering and Information Technology, University of Technology Sydney, Broadway, P.O. Box 123, 2007, Sydney, NSW, Australia

    Longbing Cao  & Guandong Xu  & 

  4. Asian Office of Aerospace Research and Development (AOARD), Air Force Office of Scientific Research (AFOSR), Air Force Research Laboratory USA, Osaka University, 7-23-17 Roppongi, 106-0032, Minato-ku, Tokyo, Japan

    Hiroshi Motoda

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Christen, P., Gayler, R.W. (2013). Adaptive Temporal Entity Resolution on Dynamic Databases. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37456-2_47

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  • DOI: https://doi.org/10.1007/978-3-642-37456-2_47

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37455-5

  • Online ISBN: 978-3-642-37456-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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