Two papers written by members of the group were presented at the International Semantic Web Conference (ISWC 2010) held in Shanghai, China from November 7th to 11th. The articles are "Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling" by Jorge Gracia del Río and "Enabling Ontology-based Access to Streaming Data Sources" by Jean-Paul Calbimonte.

OEG members have participated in both articles, Asunción Gómez-Pérez and Esther Lozano in the first one and Óscar Corcho in both of them.

In the first paper, "Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling", it is proposed to apply Semantic Web techniques to the context of conceptual modelling (more particularly to the domain of qualitative reasoning),  to smoothly interconnect conceptual models created by different users, thus facilitating the global sharing of scientific data contained in such models and creating new learning opportunities for people who start modelling. Conceptual modelling tools allow users to construct formal representations of their conceptualisations. These models are typically developed in isolation, unrelated to other user models, thus losing the opportunity of incorporating knowledge from other existing models or ontologies that might enrich the modelling process. We propose to apply Semantic Web techniques to the context of conceptual modelling (more particularly to the domain of qualitative reasoning), to smoothly interconnect conceptual models created by different users, thus facilitating the global sharing of scientific data contained in such models and creating new learning opportunities for people who start modelling. This paper describes how semantic grounding techniques can be used during the creation of qualitative reasoning models, to bridge the gap between the imprecise user terminology and a well defined external common vocabulary. We also explore the application of ontology matching techniques between models, which can provide valuable feedback during the model construction process. Link to the presentation.

In the second paper, "Enabling Ontology-based Access to Streaming Data Sources", it is focused on providing ontology-based access to streaming data sources, including sensor networks, through declarative continuous queries. We build on the existing work of r2o for enabling ontology-based access to relational data sources, and snee for query evaluation over streaming and stored data sources. This constitutes a first step towards a framework for the integration of distributed heterogeneous streaming and stored data sources through ontological models. Our approach to enable ontology-based access to streaming data consists in a service that receives queries specified in terms of the classes and properties of the ontology using sparqlStream, an extension of sparql that supports operators over rdf streams. In order to transform the SPARQL_Stream query, expressed in terms of the ontology, into queries in terms of the data sources, a set of mappings must be specified. These mappings are expressed in s2o, an extension of the r2o mapping language, which supports streaming queries and data, most notably window and stream operators. This transformation process is called query translation, and the target is the continuous query language SNEEql, which is expressive enough to deal with both streaming and stored sources. After the continuous query has been generated, the query processing phase starts, and the evaluator uses distributed query processing techniques to extract the relevant data from the sources and perform the required query processing, e.g. selection, projection, and joins. Note that query execution in sources such as sensor networks may include in-network query processing, pull or push based delivery of data between sources, and other data source specific settings. The result of the query processing is a set of tuples that the data translation process transforms into ontology instances. The prototype implementation, which extends ODEMapster, has shown the feasibility of the approach. This work constitutes a first effort towards ontology-based streaming data integration, relevant for supporting the increasing number of sensor network applications being developed and deployed in the recent years. Link to the presentation.