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The Use of Reference Graphs in the Entity Resolution of Criminal Networks

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Intelligence and Security Informatics (PAISI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 9650))

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Abstract

Entity resolution (ER) is the detection of duplicated records within a dataset representing the same real-world entity. The importance of ER is amplified within law enforcement as criminal data, or criminal networks, has inherent uncertainty and ER inaccuracy incurs a high cost. Commercial ER solutions focus on fast and scalable resolution of obvious pairs of entities, rather than the more complex non-obvious pairs which are so critical to law enforcement. Here we outline the use of proper names represented as reference graphs - generated from an algorithm that conducts name similarity, logic-based pruning, and classification using community detection and a proper name origin algorithm. The resultant classes are used at indexing and decision management stages within an ER model to support the detection of non-obvious duplicate entities. Utility is clearly demonstrated through the application of the approach on three real-world datasets of varying origin, size, topology, and heterogeneity.

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Authors and Affiliations

  1. Inland Revenue, Wellington, New Zealand

    David Robinson

Authors
  1. David Robinson
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Correspondence to David Robinson .

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Editors and Affiliations

  1. The University of Hong Kong, Hong Kong, Hong Kong

    Michael Chau

  2. Virginia Tech, Blacksburg, Virginia, USA

    G. Alan Wang

  3. The University of Arizona, Tucson, Arizona, USA

    Hsinchun Chen

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Robinson, D. (2016). The Use of Reference Graphs in the Entity Resolution of Criminal Networks. In: Chau, M., Wang, G., Chen, H. (eds) Intelligence and Security Informatics. PAISI 2016. Lecture Notes in Computer Science(), vol 9650. Springer, Cham. https://doi.org/10.1007/978-3-319-31863-9_1

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  • DOI: https://doi.org/10.1007/978-3-319-31863-9_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31862-2

  • Online ISBN: 978-3-319-31863-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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