In this paper, we present a workbench for semi-automatic ontology population from textual documents. It provides an environment for mapping the linguistic extractions with the domain ontology thanks to knowledge acquisition rules. Those rules are activated when a pertinent linguistic tag is reached. Those linguistic tags are then mapped to a concept, one of its attributes or even a semantic relation between several concepts. The rules instantiate these concepts, attributes and relations in the knowledge base constrained by the domain ontology. This paper deals with the underlying knowledge capture process and presents the first experiments realized on a real client application from the legal publishing domain.

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The French project Data to Knowledge in Agronomy and Biodiversity (D2KAB) will make available a semantically-enabled French agricultural alert newsletter. In order to describe/annotate crop phenological development stages in the newsletters, we need a specific semantic resource to semantically represent each stages. Several scales already exist to describe plant phenological development stages. BBCH, considered a reference, offers several sets of stages –one per crop called ‘individual scales’–and a general one. The French Wine and Vine Institute (IFV) has aligned several existing scales in order to identify the most useful grapevine development stages for agricultural practices. Unfortunately these scales are not available in a semantic form preventing their use in agricultural semantic applications. In this paper, we present our work of creating an ontological framework for semantic description of plant development stages and transforming specific scales into RDF vocabularies; we introduce the BBCH-based Plant Phenological Description Ontology and we illustrate this framework with four scales related to grapevine.

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Objectives: An important barrier to electronic healthcare information exchanges (HIE) is the lack of interoperability between information systems especially on the semantic level. In the scope of the ANR (Agence Nationale pour la Recherche) / TERSAN (Terminology and Data Elements Repositories for Healthcare Interoperability) project, we propose to set and use a semantic interoperability platform, based on semantic web technologies, in order to facilitate standardized healthcare information exchanges between heterogeneous Electronic Healthcare Records (EHRs) in different care settings. Material and methods:The platform is a standard-based expressive and scalable semantic interoperability framework. It includes centrally managed Common Data Elements bounded to international/national reference terminologies such as ICD10, CCAM, SNOMED CT, ICD-O, LOINC and PathLex. It offers semantic services such as dynamic mappings between reference and local terminologies. Results: A pilot implementation of semantic services was developed and evaluated within a HIE prototype in telepathology for remote expert advice. The semantic services developed for transcoding local terms into reference terms take into account the type of message and the exchange context defined within standard-based integration profiles. Conclusion:The TERSAN platform is an innovative semantic interoperability framework that (1) provides standard-based semantic services applicable to any HIE infrastructure and (2) preserves the use of local terminologies and local models by end users (health professional's priority).

Domain ontologies are a central component in the Virtuoso demonstrator, a system that captures, analyzes and aggregates open news sources in order to achieve an information position that supports complex decision processes in the context of border control. However, maintenance of the underlying ontologies is a challenging task. We demonstrate a text processing pipeline that supports domain experts in maintaining the domain ontology. The system facilitates the maintenance by generating candidate classes that should be added to the ontology. Some initial tests are carried out with filtering candidate classes from a domain specific news feed.

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[pratique] Cet article présente un moteur qui permet de recommander des « expériences à vivre » dans le domaine du tourisme en s’appuyant sur les inspirations de l’utilisateur. Le moteur compare ces inspirations aux expériences à vivre décrites dans une base de connaissances. Le système analyse des contraintes parfois complexes liées aux caractéristiques des destinations et aux souhaits de l’utilisateur afin de lui proposer la destination associée à l’expérience à vivre la plus pertinente.