Evolution des Données Liées: Maintenance des liens

Abstract

The Linked Data initiative aims at publishing structured and interlinked data on the Web by using Semantic Web technologies These technologies provide different languages for expressing data as RDF graphs and querying it with SPARQL. Linked data allow the implementation of applications that reuse data distributed on the Web. To facilitate interoperability between these applications, data issued from different providers has to be interlinked. It means that the same entity in different data sets must be identified. One of the key challenges of linked data is to deal with this heterogeneity by detecting links across datasets. In such a dynamic environment, the Web of data evolves: new data are added; outdated data are removed or changed. Then, links between data have to evolve too. Since links should not be recomputed each time a change occurs, the semantic Web needs methods that consider the evolution. Over the time, dead links can appear. Dead links are those pointing at URIs that are no longer maintained, and those that are not being set when new data is published. Too many dead links lead to a large number of unnecessary HTTP requests by applications consumers. A current research topic addressed by the Linked Data community is link maintenance. We propose in this thesis an approach to discover the links between the RDF data based on the link models that appear around the resources and ontology alignment. Our approach also includes a process to maintain links when a data change occurs. The goal of our approach is to detect correct links and erroneous links in the same database (intra-base links) and in a basic set (inter-base links). After the detection process, we propose a link maintenance method. To evaluate the performance of our approach we used the test of the 2012 OAEI evaluation campaign. We compared our approach with other systems. The obtained results show the good performance of our approach.