Aligning ontologies with subsumption and equivalence relations in Linked Data

With the profusion of RDF resources and Linked Data, ontology alignment has gained significance in providing highly comprehensive knowledge embedded in disparate sources. Ontology alignment, however, in Linking Open Data (LOD) has traditionally focused more on the instance-level rather than the schema-level. Linked Data supports schema-level alignment, provided that instance-level alignment is already established. Linked Data is a hotbed for instance-based schema alignment, which is considered a better solution for aligning classes with ambiguous or obscure names. This study proposes an instance-based schema alignment algorithm, IUT, which builds a unified taxonomy to discover subsumption and equivalence relations between two classes. A scaling algorithm is also developed that reduces pair-wise similarity computations during the taxonomy construction. The IUT is tested with DBpedia and YAGO2, and compared with two state-of-the-art schema alignment algorithms in light of four alignment tasks with different combinations of the two data sets. The experiment results show that the IUT outperforms the two algorithms in efficiency and effectiveness, and demonstrate the IUT can provide an instance-based schema alignment solution with scalability and high performance, for ontologies containing a large number of instances in LOD.