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Probabilistic Blocking with an Application to the Syrian Conflict

  • Rebecca C. SteortsEmail author
  • Anshumali Shrivastava
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11126)

Abstract

Entity resolution seeks to merge databases as to remove duplicate entries where unique identifiers are typically unknown. We review modern blocking approaches for entity resolution, focusing on those based upon locality sensitive hashing (LSH). First, we introduce k-means locality sensitive hashing (KLSH), which is based upon the information retrieval literature and clusters similar records into blocks using a vector-space representation and projections. Second, we introduce a subquadratic variant of LSH to the literature, known as Densified One Permutation Hashing (DOPH). Third, we propose a weighted variant of DOPH. We illustrate each method on an application to a subset of the ongoing Syrian conflict, giving a discussion of each method.

Notes

Acknowledgments

We would like to thank HRDAG for providing the data and for helpful conversations. We would also like to thank Stephen E. Fienberg and Lars Vilhuber for making this collaboration possible. Steorts’s work is supported by NSF-1652431 and NSF-1534412. Shrivastava’s work is supported by NSF-1652131 and NSF-1718478. This work is representative of the author’s alone and not of the funding organizations.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Statistical Science, Affiliated Faculty, Computer Science, Biostatistics and Bioinformatics, the Information Initiative at Duke (iiD), and the Social Science Research Institute (SSRI)Duke UniversityDurhamUSA
  2. 2.Department of Computer ScienceRice UniversityHoustonUSA

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