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Linked Open Data exhibits growth in both volume and variety of publisheddata. Due to this variety, instances of many different types (e.g. Person) canbe found in published datasets. Type alignment is the problem of automaticallymatching 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 sameunderlying entity. By performing type alignment a priori, only instancesconforming to aligned types are processed together, leading to significantsavings. This article describes a type alignment experience with twolarge-scale cross-domain RDF knowledge graphs, DBpedia and Freebase, thatcontain hundreds, or even thousands, of unique types. Specifically, we presenta MapReduce-based type alignment algorithm and show that there are at leastthree reasonable ways of evaluating type alignment within the larger context ofinstance matching. We comment on the consistency of those results, and notesome general observations for researchers evaluating similar algorithms oncross-domain graphs.

Experience: Type alignment on DBpedia and Freebase