We have developed a platform running on a large multi-touch and multi-users table. Its intended application domain is the collaborative preliminary design phase of engineering projects. We also developed an ontology describing the virtual objects of the platform and the projects on which users collaborate. In this paper we first present the originality of this ontology. It extends the DOLCE-CORE ontology in order to model the true nature of the virtual objects that the users can manipulate on the device and the users’ actions. We then present the different roles played by the ontology in the development of the platform. It serves as a reference of the semantics for the designers of the collaborative platform and as a model of the environment including table, participants, projects, etc. This model is mainly used by the intelligent agents of a multi-agent system whose objective is to support the participants during a working session around the table.

L'objectif du projet TATIN-PIC (TAble Tactile Interactive et Plateforme Intelligente de Conception), est de developper une plateforme exploitant une table interactive multi-tactile et multi-modale dediee a la conception preliminaire. A cette fin, une ontologie – OntoTATIN-PIC – est construite pour representer les connaissances liees aux dispositifs et a l'activite de conception. Dans cet article, nous presentons, d'une part, l'originalite de cette ontologie : OntoTATIN-PIC etend l'ontologie fondatrice DOLCE-CORE pour rendre compte precisement de la nature des objets manipules par les utilisateurs sur la table interactive et definir rigoureusement les actions realisees. D'autre part, nous presentons les differents roles complementaires joues par l'ontologie dans le developpement de la plateforme.

Cet article presente une methode originale de calcul de prototypicalite lexicale, c'est-a-dire une mesure de difference de representativite des termes denotant un concept au sein d'une ontologie a partir d'un corpus de reference. Nous proposons deux champs d'applications de ce gradient : (1) l'analyse d'un corpus de textes, et (2) la determination a priori ou le controle a posteriori du choix des termes vedettes et des synonymes dans la terminologie des ontologies. Nous presentons egalement les resultats obtenus lors d'une experimentation menee a partir d'une ontologie des maladies rares (ONTOORPHA) et d'un corpus de textes composes des resumes des differentes entrees du portail des maladies rares ORPHANET, et confirmant l'interet de notre approche sur les deux champs d'application presentes.

In this paper, we introduce an application of Proxima and define a new measure of proximity between two concepts present in an ontology. The approach is based on the three dimensions of a conceptualization: intention with relations between concepts, expression with terms denoting concepts, and extension with instances of concepts. This preliminary work, in the field of rare diseases, involved the Orphanet Ontology of Rare Diseases (OntoOrpha) and corpus of texts extracted from Online Inheritance in Man (OMIM). The proximity measurements are consistent with an appropriate representation of groups of diseases in the ontology, which are derived from the Orphanet classifications of rare diseases. Other semantic relations are explored and new perspectives in medical knowledge curation are proposed.

Cet article presente deux nouvelles mesures de similarite et proximite entre concepts d'une ontologie, qui ont pour originalite, d'une part, de bien distinguer ces deux notions et, d'autre part, de tirer parti des trois composantes semiotiques des concepts : intension, extension et expression. Les calculs de proximite et de similarite reposent donc sur les proprietes des concepts, sur leurs instances et sur leurs termes. Des ressources additionnelles a l'ontologie sont egalement utilisees, en particulier un corpus de textes. La mesure SEMIOSEM evalue la similarite entre deux concepts, c'est-a-dire leur analogie, en s'appuyant sur la comparaison entre leurs prototypes et sur le fait qu'ils partagent des instances communes et des termes communs. La mesure PROXIMA evalue la proximite entre deux concepts, c'est-a-dire le fait qu'ils soient cognitivement lies, en mesurant la densite des proprietes qui les relient, et l'apparition conjointe dans un corpus de textes des termes designant ces concepts ou leurs instances. Nous presentons egalement les premiers tests realises qui permettent de comparer nos mesures avec les mesures de similarite existantes et de comparer SEMIOSEM et PROXIMA entre elles.

The Green Computing Observatory (GCO) addresses the previous issues within the framework of a production infrastructure dedicated to e-science, providing a unique facility for the Computer Science and Engineering community. The first barrier to improved energy efficiency is the difficulty of collecting data on the energy consumption of individual components of data centers, and the lack of overall data collection. GCO collects monitoring data on energy consumption of a large computing center, and publish them through the Grid Observatory. These data include the de tailed monitoring of the processors and motherboards, as well as the global site information, such as overall consumption and overall cooling, as optimizing at the global level is a promising way of research. A second barrier is making the collected data usable. The difficulty is to make the data readily consistent and complete, as well as understandable for further exploitation. For this purpose, GCO opts for an ontological approach in order to rigorously define the semantics of the data (what is measured) and the context of their production (how are they acquired and/or calculated).

This paper introduces the notion of prototypicality in Ontology Engineering. Three kinds of prototypicality are considered: a concept can be more or less representative of its super-concept (conceptual prototypicality); a term can be more or less associated to a concept (lexical prototypicality); an instance can be more or less representative of its concept (instance prototypicality). Prototypicalities are modeled as order relations which allow to modulate links between concepts, terms and instances within an ontology. To calculate prototypicality gradients used to quantify these orders, we advocate a specific method which is based on all the components of an ontology (i.e. concepts, properties and instances) and a corpus. This paper also underlines the relevance of prototypicality for improving the efficiency of Ontology Engineering processes, in particular Ontology Personalization and Semantic-based Information Retrieval.

This paper introduces a new approach dedicated to the Ontology Personalization.Inspired by works in Cognitive Psychology, our work is based on a process which aims at capturing the user-sensitive relevance of the categorization process, that is the one which is really perceived by the end-user.Practically, this process consists in decorating the Specialization/Generalization links (i.e. the is-a links) of the hierarchy of concepts with 2 gradients.The goal of the first gradient, called Conceptual Prototypicality Gradient, is to capture the user-sensitive relevance of the categorization process, that is the one which is perceived by the end-user.As this gradient is defined according to the three aspects of the semiotic triangle (i.e.intentional, extensional and expressional dimension), we call it Semiotic based Prototypicality Gradient.The objective of the second gradient, called Lexical Prototypicality Gradient, is to capture the user-sensitive relevance of the lexicalization process, i.e. the definition of a set of terms used to denote a concept.These gradients enrich the initial formal semantics of an ontology by adding a pragmatics defined according to a context of use which depends on parameters like culture, educational background and/or emotional context of the end-user.This paper also introduces a new similarity measure also defined in the context of a semiotic-based approach.The first originality of this measure, called SEMIOSEM, is to consider the three semiotic dimensions of the conceptualization underlying an ontology.Thus, SEMIOSEM aims at aggregating and improving existing extensional-based and intentional-based measures.The second originality of this measure is to be context-sensitive, and in particular user-sensitive.This makes SEMIOSEM more flexible, more robust and more close to the end-user's judgment than the other similarity measures which are usually only based on one aspect of a conceptualization and never take the end-user's perceptions and purposes into account.

A telephone accessory for detecting and recording and optionally displaying the number of rings sounded by a telephone which is ringing is disclosed. The accessory includes detecting means to detect the sound of the rings of the telephone, counting means associated with the detecting means to count the number of rings sounded by the telephone; recording/storage means associated with the counting means to record/store the number of rings counted; and optionally display means associated with the recording/storing means to display the number of rings recorded/stored. A telephone accessory according to the invention can be a relatively inexpensive electronic circuit which does not include tape drives. The accessory can indicate that a particular caller has called without the telephone receiver being disengaged and in these circumstances a call by the caller does not cost anything. The telephone accessory also has a number of other advantages over telephone answering machines namely that a caller does not have to listen to a prerecorded message or leave a verbal message in order to indicate that he or she has made a telephone call.

Abstract : Cet article pr´ esente une m´ ethode originale de personnalisation des ontologies principalement d´ edi´ ee ` a la personnalisation des SI ` a base d’ontologie. Cette m´ ethode s’appuie sur l’ajout, ` a l’ontologie, de connaissances suppl´ ementaires propres ` a l’utilisateur mais respectant la s´ emantique,exprim´ ee dans l’ontologie. Ces connaissances expriment des prototypicalit´ es, c’est-` a-dire des repr´ esentativit´ es entre deux concepts ou entre un terme et le concept qu’il d´ esigne. Nous pro- posons de calculer ces prototypicalit´ es ` a partir des connaissances pr´ esentes dans l’ontologie et communes ` a tous les utilisateurs, et ` a partir de ressources pro- pres ` a l’utilisateur, ` a savoir des instances de concepts, un corpus de textes et des pond´ erations fix´ ees par l’utilisateur et exprimant l’importance des propri´ et´ es dans la d´ efinition des concepts. Les premi` eres exp´ erimentations, men´ ees ` a l’aide d’un outil d´ edi´ e appel´ e TooPrag, confirment l’int´ erˆ et de notre approche. Mots-cl´ es : Personnalisation, Prototypicalit´ e, S´ emiotique

This paper introduces a new similarity measure called SEMIOSEM The first originality of this measure, which is defined in the context of a semiotic-based approach, is to consider the three dimensions of the conceptualization underlying a domain ontology: the intension (i.e. the properties used to define the concepts), the extension (i.e. the instances of the concepts) and the expression (i.e. the terms used to denote both the concepts and the instances). Thus, SEMIOSEM aims at aggregating and improving existing extensional-based and intensional-based measures, with an original expressional one. The second originality of this measure is to be context-sensitive, and in particular user-sensitive. Indeed. SEMIOSEM is based on multiple informations sources: (1) a textual corpus, validated by the end-user, which must reflect the domain underlying the ontology which is considered, (2) a set of instances known by the end-user, (3) an ontology enriched with the perception of the end-user on how each property associated to a concept c is important for defining c and (4) the emotional state of the end-user. The importance of each source can be modulated according to the context of use and SEMIOSEM remains valid even if one of the source is missing. This makes our measure more flexible, more robust and more close to the end-user's judgment than the other similarity measures which are usually only based on one aspect of a conceptualization and never take the end-user's perceptions and purposes into account.

With the current emergence of Cognitive Sciences and the development of Knowledge Management applications in Social and Human Sciences, Subjective Knowledge becomes an unavoidable subject and a real challenge, which must be integrated and developed in Ontology Engineering and Ontology-based Information Retrieval. This paper introduces a new approach dedicated to the Personalization of a Domain Ontology. Inspired by works in Cognitive Psychology, our work is based on a process which aims at capturing the user-sensitive degree of truth of the categorisation process, that is the one which is really perceived by the end-user. Practically, this process consists in decorating the Specialisation/Generalisation links (i.e. the ISA links) of the hierarchy of concepts with a specific gradient. As this gradient is defined according to the three aspects of the semiotic triangle (i.e. intensional, extensional and expressional dimension), we call it Semiotic-based Prototypicality Gradient. It enrichs the initial formal semantics of an ontology by adding a pragmatics defined according to a context of use which depends on parameters like culture, educational background and/or emotional context of the end-user.

This paper introduces a new approach of ontology matching named axiom-based ontology matching . As this approach is founded on the use of axioms, it is mainly dedicated to heavyweight ontology, but it can also be applied to lightweight ontology as a complementary approach to the current techniques based on the analysis of natural language expressions, instances and/or taxonomical structures of ontologies. This new matching paradigm is defined in the context of the Conceptual Graphs model (CG).

Inspired by works in Cognitive Psychology, this paper advocates the definition of a specific process dedicated to Ontology Personalization. Practically, this process consists in decorating the Specialisation/Generalisation links (i.e. the is-a links) of the hierarchy of concepts with 2 gradients. The goal of the first gradient, called Conceptual Prototypicality Gradient, is to capture the user-sensitive degree of truth of the categorisation process, that is the one which is perceived by the end-user. As this gradient is defined according to the three aspects of the semiotic triangle (i.e. intentional, extensional and expressional dimension), we call it Semiotic-based Prototypicality Gradient. The objective of the second gradient, called Lexical Prototypicality Gradient, is to capture the user-sensitive degree of truth of the lexicalisation process, i.e. the definition of a set of terms used to denote a concept. These gradients aim at representing the multiple points of view that can be associated to the same ontology. They enrich the initial formal semantics of an ontology by adding a pragmatics defined according to a context of use which depends on parameters like culture, educational background or emotional context.

Managing multiple ontologies is now a core question in most of the applications that require semantic interoperability. The semantic Web is surely the most significant application of this report: the current challenge is not to design, develop and deploy domain ontologies but to define semantic correspondences among multiple ontologies covering overlapping domains. In this paper, we introduce a new approach of ontology matching named axiom-based ontology matching. As this approach is founded on the use of axioms, it is mainly dedicated to heavyweight ontologies, but it can also be applied to lightweight ontologies as a complementary approach to the current techniques based on the analysis of natural language expressions, instances and/or taxonomical structures of ontologies. This new matching paradigm is defined in the context of the conceptual graphs model (CG), where the projection (i.e. the main operator for reasoning with CG which corresponds to homomorphism of graphs) is used as a means to semantically match the concepts and the relations of two ontologies through the explicit representation of the axioms in terms of conceptual graphs.

Subjective knowledge becomes an unavoidable subject, which must be integrated and developped in Ontological Engineering. REDENE is a project which is based on (i) the formalization of some results defined in Cognitive Psychology on human memory by considering assumptions established in the field of the Neurosciences and (ii) the integration and the exploitation of such a formalisation within the processes dedicated information retrieval based on the use of ontologies. MOTS-CLES : Ontologie Pragmatisee Vernaculaire du Domaine, Prototypicalite Conceptuelle et Lexicale, Categorisation, Gradient, Personnalisation, Adaptation, Pragmatique.