In many applications, it is convenient to substitute a large data graph with a smaller homomorphic graph. This paper investigates approaches for summarising massive data graphs. In general, massive data graphs are processed using a shared-nothing infrastructure such as MapReduce. However, accurate graph summarisation algorithms are suboptimal for this kind of environment as they require multiple iterations over the data graph. We investigate approximate graph summarisation algorithms that are efficient to compute in a shared-nothing infrastructure. We define a quality assessment model of a summary with regards to a gold standard summary. We evaluate over several datasets the trade-offs between efficiency and precision of the algorithms. With regards to an application, experiments highlight the need to trade-off the precision and volume of a graph summary with the complexity of a summarisation technique.

We demonstrate Sig.ma, both a service and an end user application to access the Web of Data as an integrated information space.

Currently a large number of Web sites are driven by Content Management Systems (CMS) which manage textual and multimedia content but also - inherently - carry valuable information about a site’s structure and content model. Exposing this structured information to the Web of Data has so far required considerable expertise in RDF and OWL modelling and additional programming effort. In this paper we tackle one of the most popular CMS: Drupal. We enable site administrators to export their site content model and data to the Web of Data without requiring extensive knowledge on Semantic Web technologies. Our modules create RDFa annotations and – optionally – a SPARQL endpoint for any Drupal site out of the box. Likewise, we add the means to map the site data to existing ontologies on the Web with a search interface to find commonly used ontology terms. We also allow a Drupal site administrator to include existing RDF data from remote SPARQL endpoints on the Web in the site. When brought together, these features allow networked RDF Drupal sites that reuse and enrich Linked Data. We finally discuss the adoption of our modules and report on a use case in the biomedical field and the current status of its deployment. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Object-oriented programming is the current mainstream programming paradigm but existing RDF APIs are mostly triple-oriented. Traditional techniques for bridging a similar gap between relational databases and object-oriented programs cannot be applied directly given the different nature of Semantic Web data, for example in the semantics of class membership, inheritance relations, and object conformance to schemas.