A DNF Blocking Scheme Learner for Heterogeneous Datasets
Abstract
Entity Resolution concerns identifying co-referent entity pairs across datasets. A typical workflow comprises two steps. In the first step, a blocking method uses a one-many function called a blocking scheme to map entities to blocks. In the second step, entities sharing a block are paired and compared. Current DNF blocking scheme learners (DNF-BSLs) apply only to structurally homogeneous tables. We present an unsupervised algorithmic pipeline for learning DNF blocking schemes on RDF graph datasets, as well as structurally heterogeneous tables. Previous DNF-BSLs are admitted as special cases. We evaluate the pipeline on six real-world dataset pairs. Unsupervised results are shown to be competitive with supervised and semi-supervised baselines. To the best of our knowledge, this is the first unsupervised DNF-BSL that admits RDF graphs and structurally heterogeneous tables as inputs.
Cite
@article{arxiv.1501.01694,
title = {A DNF Blocking Scheme Learner for Heterogeneous Datasets},
author = {Mayank Kejriwal and Daniel P. Miranker},
journal= {arXiv preprint arXiv:1501.01694},
year = {2015}
}