  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.
