In the latest years, there has been a huge e ort to deploy large amounts of data, making it available in the form of RDF data thanks, among others, to the Linked Data initiative. In this context, using shared ontologies has been crucial to gain interoperability, and to be able to integrate and exploit third party datasets. However, using the same ontology does not su ce to successfully query or integrate external data within your own dataset: the actual usage of the vocabulary (e.g., which concepts have instances, which properties are actually populated and how, etc.) is crucial for these tasks. Being able to compare di erent RDF graphs at the actual usage level would indeed help in such situations. Unfortunately, the complexity of graph comparison is an obstacle to the scalability of many approaches. In this article, we present our structural similarity measure, designed to compare structural similarity of low-level data between two di erent RDF graphs according to the pa erns they share. To obtain such pa erns, we leverage a data mining method (KRIMP) which allows to extract the most descriptive pa erns appearing in a transactional database. We adapt this method to the particularities of RDF data, proposing two di erent conversions for an RDF graph. Once we have the descriptive pa erns, we evaluate how much two graphs can compress each other to give a numerical measure depending on the common data structures they share. We have carried out several experiments to show its ability to capture the structural di erences of actual vocabulary usage.

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A large number of semantic Web knowledge bases have been developed and published on the Web. To help the user identify the knowledge bases relevant for a given problem, and estimate their usability, we propose a declarative indexing framework and an associated visualization Web application, KartoGraphI. It provides an overview of important characteristics for more than 400 knowledge bases including, for instance, dataset location, SPARQL compatibility level, shared vocabularies, etc.

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