  Linked Open Data exhibits growth in both volume and variety of published
data. Due to this variety, instances of many different types (e.g. Person) can
be found in published datasets. Type alignment is the problem of automatically
matching types (in a possibly many-many fashion) between two such datasets.
Type alignment is an important preprocessing step in instance matching.
Instance matching concerns identifying pairs of instances referring to the same
underlying entity. By performing type alignment a priori, only instances
conforming to aligned types are processed together, leading to significant
savings. This article describes a type alignment experience with two
large-scale cross-domain RDF knowledge graphs, DBpedia and Freebase, that
contain hundreds, or even thousands, of unique types. Specifically, we present
a MapReduce-based type alignment algorithm and show that there are at least
three reasonable ways of evaluating type alignment within the larger context of
instance matching. We comment on the consistency of those results, and note
some general observations for researchers evaluating similar algorithms on
cross-domain graphs.
