Detection of Related Semantic Datasets Based on Frequent Subgraph Mining.

We describe an approach to nd similarities between RDF datasets, which may be applicable to tasks such as link discovery, dataset summarization or dataset understanding. Our approach builds on the assumption that similar datasets should have a similar structure and include semantically similar resources and relationships. It is based on the combination of Frequent Subgraph Mining (FSM) techniques, used to synthesize the datasets and nd similarities among them. The result of this work can be applied for easing the task of data interlinking and for promoting data reusing in the Semantic Web.