English

Probabilistic Blocking with An Application to the Syrian Conflict

Databases 2018-10-15 v1 Machine Learning Applications Machine Learning

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 kk-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.

Keywords

Cite

@article{arxiv.1810.05497,
  title  = {Probabilistic Blocking with An Application to the Syrian Conflict},
  author = {Rebecca C. Steorts and Anshumali Shrivastava},
  journal= {arXiv preprint arXiv:1810.05497},
  year   = {2018}
}

Comments

16 pages, 3 figures. arXiv admin note: substantial text overlap with arXiv:1510.07714, arXiv:1710.02690

R2 v1 2026-06-23T04:37:37.453Z