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HiDER: Query-Driven Entity Resolution for Historical Data

  • Bijan Ranjbar-Sahraei
  • Julia Efremova
  • Hossein Rahmani
  • Toon Calders
  • Karl Tuyls
  • Gerhard Weiss
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9286)

Abstract

Entity Resolution (ER) is the task of finding references that refer to the same entity across different data sources. Cleaning a data warehouse and applying ER on it is a computationally demanding task, particularly for large data sets that change dynamically. Therefore, a query-driven approach which analyses a small subset of the entire data set and integrates the results in real-time is significantly beneficial. Here, we present an interactive tool, called HiDER, which allows for query-driven ER in large collections of uncertain dynamic historical data. The input data includes civil registers such as birth, marriage and death certificates in the form of structured data, and notarial acts such as estate tax and property transfers in the form of free text. The outputs are family networks and event timelines visualized in an integrated way. The HiDER is being used and tested at BHIC center(Brabant Historical Information Center, https://www.bhic.nl); despite the uncertainties of the BHIC input data, the extracted entities have high certainty and are enriched by extra information.

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References

  1. 1.
    Altwaijry, H., Kalashnikov, D.V., Mehrotra, S.: Query-driven approach to entity resolution. Proceedings of the VLDB Endowment 6(14), 1846–1857 (2013)Google Scholar
  2. 2.
    Efremova, J., Ranjbar-Sahraei, B., Rahmani, H., Oliehoek, F.A., Calders, T., Tuyls, K.: Multi-source entity resolution for genealogical data. In: Population Reconstruction. Springer (2015) (in press)Google Scholar
  3. 3.
    Rahmani, H., Ranjbar-Sahraei, B., Weiss, G., Tuyls, K.: Entity resolution in disjoint graphs: an application on genealogical data. Intelligent Data Analysis 20(2) (2016) (in press)Google Scholar
  4. 4.
    Rahmani, H., Ranjbar-Sahraei, B., Weiss, G., Tuyls, K.: Contextual entity resolution approach for genealogical data. In: Workshop on Knowledge Discovery, Data Mining and Machine Learning, Aachen, Germany (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Bijan Ranjbar-Sahraei
    • 1
  • Julia Efremova
    • 2
  • Hossein Rahmani
    • 1
  • Toon Calders
    • 3
  • Karl Tuyls
    • 4
  • Gerhard Weiss
    • 1
  1. 1.Maastricht UniversityMaastrichtThe Netherlands
  2. 2.Eindhoven University of TechnologyEindhovenThe Netherlands
  3. 3.Université Libre de BruxellesBrusselsBelgium
  4. 4.University of LiverpoolLiverpoolUK

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