Abstract. This paper concerns the design of a workflow which permits to feed and query a data warehouse opened on the Web, driven by a domain ontology. This data warehouse has been built to enrich local data sources and is composed of data tables extracted from Web documents. We recall the main steps of our semi-automatic method to annotate Web data tables driven by a domain ontology. The output of this method is an XML/RDF data warehouse composed of XML documents representing Web data tables with their fuzzy RDF annotations. We then present how to query simultaneously the local data sources and the XML/RDF data warehouse, using the domain ontology, through a flexible querying language. This language allows preferences to be expressed in selection criteria using fuzzy sets. We study more precisely how to retrieve approximate answers extracted from the Web data tables by comparing preferences expressed as fuzzy sets with fuzzy annotations using SPARQL.

Collecting and analyzing interaction traces in Technology Enhanced Learning (TEL) environments is a common practice of researchers wishing to optimize the efficiency of these environments. This paper proposes a new approach, introduced by the proxy model, to the challenge of sharing and analyzing contextualized learning traces corpora.

Researchers wishing to analyze Technology Enhanced Learning (TEL) situations usually collect interaction traces produced by TEL environments. This paper addresses the issue of sharing, between researchers using TEL environments, of contextualized interaction trace corpora and analysis tools of these corpora. We present a new ontology-based approach called the "proxy approach" to address this issue of sharing.

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International audience—Sharing and analyzing data collected within Technology Enhanced Learning environments is an interesting issue for researchers to validate their models and systems. In this paper we present a corpus we built and analyzed in order to validate our proposed " Proxy approach " as an approach for sharing and analyzing learning data corpora