Multi-source uncertain entity resolution: Transforming holocaust victim reports into people

Tomer Sagi, Avigdor Gal, Omer Barkol, Ruth Bergman, Alexander Avram

Research output: Contribution to journalArticlepeer-review


In this work we present a multi-source uncertain entity resolution model and show its implementation in a use case of Yad Vashem, the central repository of Holocaust-era information. The Yad Vashem dataset is unique with respect to classic entity resolution, by virtue of being both massively multi-source and by requiring multi-level entity resolution. With today's abundance of information sources, this project motivates the use of multi-source resolution on a big-data scale. We instantiate the proposed model using the MFIBlocks entity resolution algorithm and a machine learning approach, based upon decision trees to transform soft clusters into ranked clustering of records, representing possible entities. An extensive empirical evaluation demonstrates the unique properties of this dataset that make it a good candidate for multi-source entity resolution. We conclude with proposing avenues for future research in this realm.

Original languageEnglish
Pages (from-to)124-136
Number of pages13
JournalInformation Systems
StatePublished - 1 Apr 2017
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2016 Elsevier Ltd


  • Blocking
  • Holocaust
  • Uncertain entity resolution

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture


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