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In this paper, we show the different steps of an annotation process that allows one to annotate data tables with the relations of a domain ontology. The columns of a table are first segregated according to whether they represent numeric or symbolic data. Then, we annotate the numeric columns with their corresponding numeric type, and the symbolic columns with their corresponding symbolic type, combining different evidences from the ontology. The relations represented by a table are recognized using both the table title and the types of the columns. We give experimental results for our annotation method. [ABSTRACT FROM AUTHOR]

The MIEL++ system integrates data expressed in two different formalisms: a relational database and an XML database. The XML database is filled with data semi-automatically retrieved from the Web, which have been semantically enriched according to the ontology used in the relational database. These data may be imprecise and represented as possibility distributions. The MIEL++ querying system scans the two databases simultaneously in a transparent way for the end-user. To scan the XML database, the MIEL query is translated into an XML tree query. In this paper, we propose to introduce flexibility into the query processing of the XML database, in order to take into account the imperfections due to the semantic enrichment of its data. This flexibility relies on fuzzy queries and query rewriting which consists in generating a set of approximate queries from an original query using three transformation techniques: deletion, renaming and insertion of query nodes. [ABSTRACT FROM AUTHOR]

This paper presents a new ontology mapping method. This method addresses the case in which a non-structured ontology is to be mapped with a structured one. Both ontologies are composed of triplets of the form (object, characteristic, value). Structured means that the values describing the objects according to a given characteristic are hierarchically organized using the a kind of relation. The proposed method uses fuzzy conceptual graphs [8] to represent and map objects from a source ontology to a target one. First, we establish a correspondence between characteristics of the source ontology and characteristics of the target ontology based on the comparison of their associated values. Then, we propose an original way of translating the description of an object of the source ontology using characteristics and values of the target ontology. The description thus translated is represented as a fuzzy conceptual graph. Finally, a new projection operation is used to find mappings between translated objects and actual objects of the target ontology. This method has been implemented and the results of an experimentation concerning the mapping of ontologies in the field of risk in food are presented. [ABSTRACT FROM AUTHOR]