On Linking Heterogeneous Dataset Collections

Link discovery is the problem of linking entities between two or more datasets, based on some (possibly unknown) specification. A blocking scheme is a one-to-many mapping from entities to blocks. Blocking methods avoid O(n) comparisons by clustering entities into blocks, and limiting the evaluation of link specifications to entity pairs within blocks. Current link-discovery blocking methods explicitly assume that two RDF datasets are provided as input, and need to be linked. In this paper, we assume instead that two heterogeneous dataset collections, comprising arbitrary numbers of RDF and tabular datasets, are provided as input. We show that data model heterogeneity can be addressed by representing RDF datasets as property tables. We also propose an unsupervised technique called dataset mapping that maps datasets from one collection to the other, and is shown to be compatible with existing clustering methods. Dataset mapping is empirically evaluated on three real-world test collections ranging over government and constitutional domains, and shown to improve two established baselines.