@conference {kouki:icdm17, title = {Collective Entity Resolution in Familial Networks}, booktitle = {IEEE International Conference on Data Mining (ICDM)}, year = {2017}, note = {To Appear}, abstract = {

Entity resolution in settings with rich relational structure often introduces complex dependencies between coreferences. Exploiting these dependencies is challenging {\textendash} 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, and logical constraints, such as transitivity and bijective matching, in a collective model. We show how to integrate these features using probabilistic soft logic, a scalable probabilistic programming framework. In experiments on realworld data, our model significantly outperforms state-of-theart classifiers that use relational features but are incapable of collective reasoning. I

}, url = {https://github.com/pkouki/icdm2017}, author = {Kouki, Pigi and Pujara, Jay and Marcum, Christopher and Koehly, Laura and Lise Getoor} }