Abstract

Entity resolution in settings with rich relational structure often introduces complex dependencies between co-references. Exploiting these dependencies is challenging—it requires seamlessly combining statistical, relational, and logical dependencies. One task of particular interest is entity resolution in familial networks. In this setting, multiple partial representations of a family tree are provided, from the perspective of different family members, and the challenge is to reconstruct a family tree from these multiple, noisy, partial views. This reconstruction is crucial for applications such as understanding genetic inheritance, tracking disease contagion, and performing census surveys. Here, we design a model that incorporates statistical signals (such as name similarity), relational information (such as sibling overlap), logical constraints (such as transitivity and bijective matching), and predictions from other …

Date

2019

Authors

Pigi Kouki, Jay Pujara, Christopher Marcum, Laura Koehly, Lise Getoor

Journal

Knowledge and Information Systems

Volume

61

Issue

3

Pages

1547-1581

Publisher

Springer London