An extension of ontology based databases to handle preferences

1th International Conference on Enterprise Information Systems; Milan; Italy; 6 May 2009 through 10 May 2009Ontologies have been defined to make explicit the semantics of data. With the emergence of the SemanticWeb, the amount of ontological data (or instances) available has increased. To manage such data, Ontology Based DataBases (OBDBs), that store ontologies and their instance data in the same repository have been proposed. These databases are associated with exploitation languages supporting description, querying, etc. on both ontologies and data. However, usually queries return a big amount of data that may be sorted in order to find the relevant ones. Moreover, in the current, few approaches considering user preferences when querying have been developed. Yet this problem is fundamental for many applications especially in the e-commerce domain. In this paper, we first propose an extension of an existing OBDB, called OntoDB through extension of their ontology model in order to support semantic description of preferences. Secondly, an extension of an ontology based query language, called OntoQL defined on OntoDB for querying ontological data with preferences is presented. Finally, an implementation of the proposed extensions are described

Handling domain knowledge in system design models. An ontology based approach.

Complex systems models are designed in heterogeneous domains and this heterogeneity is rarely considered explicitly when describing and validating processes. Moreover, these systems usually involve several domain experts and several design models corresponding to different analyses (views) of the same system. However, no explicit information regarding the characteristics neither of the domain nor of the performed system analyses is given. In our thesis, we propose a general framework offering first, the formalization of domain knowledge using ontologies and second, the capability to strengthen design models by making explicit references to the domain knowledgeformalized in these ontology. This framework also provides resources for making explicit the features of an analysis by formalizing them within models qualified as ‘’points of view ‘’. We have set up two deployments of our approach: a Model Driven Engineering (MDE) based deployment and a formal methods one based on proof and refinement. This general framework has been validated on several no trivial case studies issued from system engineering