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1- State of the art on data integration techniques and usage policies<\/b><\/p>\n
This report presents the state of the art of the two main issues to construct the decentralized and semantic learning Infrastructure for lifelong learning, i.e. semantic personal data integration and trusted data sharing.<\/p>\n
ODMTP (On Demand Mapper with Triple pattern matching) enables triple pattern matching over non-RDF datasources.<\/p>\n
Live demos available for:<\/strong><\/p>\n Demos presented at:<\/strong><\/p>\n Web applications facilitate combining resources (linked data, web services, source code, documents, etc.) to create new ones. For a\u00a0resource producer, choosing the appropriate license for a combined resource is not easy. It involves choosing a license compliant with all the licenses of combined resources and analyzing the reusability of the resulting resource through the compatibility of its license. The risk is either, to choose a license too restrictive making the resource difficult to reuse or to choose a not enough restrictive license that will not sufficiently protect the resource. Finding the right trade-off between compliance and compatibility is a difficult process. An automatic ordering over licenses would facilitate this task. Our research question is: given a license li, how to automatically position li over a set of licenses in terms of compatibility and compliance? We propose CaLi, a model that partially orders licenses. Our approach uses restrictiveness relations among licenses to define compatibility and compliance. We validate experimentally CaLi with a quadratic algorithm and show its usability through a prototype of a license-based search engine. Our work is a step towards facilitating and encouraging the publication and reuse of licensed resources in the Web of Data.<\/p>\n Live demos:<\/strong><\/p>\n Federated SPARQL queries allow to query multiple interlinked datasets hosted by remote SPARQL endpoints. However, finding federated queries over a growing number of datasets is challenging. In this paper, we propose PFed, an approach to recommend plausible federated queries based on real query logs of different datasets. The problem is not to find similar federated queries, but plausible complementary queries over different datasets. Starting with a real SPARQL query from a given log, PFed stretches the query with real queries from different logs. To prune the research space, PFed proposes semantic summary to prune the query logs. Experimental results with real logs of DBpedia and SWDF demonstrate that PFed is able to prune drastically the logs and\n
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3- A classification model for RDF datasets licenses<\/h4>\n
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4- Recommending Plausible Federated SPARQL queries<\/h4>\n
\nrecommend plausible federated queries.<\/p>\n\n