15 research outputs found
Effect of using sponge pieces in a solar still
Solar distillation is a very effective way to obtain pure water, especially in isolated areas where the water is infected or polluted to obtain drinking water. Two conventional solar stills of the same size (0.5 x 0.5 m) were tested for 8 hours. One still is priced as an SSR reference still and the other still which contains pieces of sponge is SSM and that is the subject of our work. The results show that the use of sponge in winter improves the yield of 10 %
From Natural Language Requirements to Formal Specification Using an Ontology
In order to check requirement specifications written in natural language, we have chosen to model domain knowledge through an ontology and to formally represent user requirements by its population. Our approach of ontology population focuses on instance property identification from texts. We do so using extraction rules automatically acquired from a training corpus and a bootstrapping terminology. These rules aim at identifying instance property mentions represented by triples of terms, using lexical, syntactic and semantic levels of analysis. They are generated from recurrent syntactic paths between terms denoting instances of concepts and properties. We show how focusing on instance property identification allows us to precisely identify concept instances explicitly or implicitly mentioned in texts
An ontology for the conceptualization of an intelligent environment and its operation
International audienceNowadays sensors and actuators are increasingly used in different spaces, creating an intelligent environment. This article aims to describe a conceptualization of an intelligent environment and its operation, in order to check its consistency and its conformity. This conceptualization is done through an ontology representing the domain knowledge, whose elements will be instantiated from natural language texts describing the physical configuration of an intelligent environment and a scenario describing the operation desired by the user of the environment. We chose OWL to represent formally our environment augmented with SWRL rules to represent the dynamic aspect of the operation system and SQWRL to query our conceptual model. We show how consistency and conformity are checked thanks to this formalism
Des spécifications en langage naturel aux spécifications formelles via une ontologie comme modÚle pivot
Le dĂ©veloppement d'un systĂšme a pour objectif de rĂ©pondre Ă des exigences. Aussi, le succĂšs de sa rĂ©alisation repose en grande partie sur la phase de spĂ©cification des exigences qui a pour vocation de dĂ©crire de maniĂšre prĂ©cise et non ambiguĂ« toutes les caractĂ©ristiques du systĂšme Ă dĂ©velopper.Les spĂ©cifications d'exigences sont le rĂ©sultat d'une analyse des besoins faisant intervenir diffĂ©rentes parties. Elles sont gĂ©nĂ©ralement rĂ©digĂ©es en langage naturel (LN) pour une plus large comprĂ©hension, ce qui peut mener Ă diverses interprĂ©tations, car les textes en LN peuvent contenir des ambiguĂŻtĂ©s sĂ©mantiques ou des informations implicites. Il n'est donc pas aisĂ© de spĂ©cifier un ensemble complet et cohĂ©rent d'exigences. D'oĂč la nĂ©cessitĂ© d'une vĂ©rification formelle des spĂ©cifications rĂ©sultats.Les spĂ©cifications LN ne sont pas considĂ©rĂ©es comme formelles et ne permettent pas l'application directe de mĂ©thodes vĂ©rification formelles.Ce constat mĂšne Ă la nĂ©cessitĂ© de transformer les spĂ©cifications LN en spĂ©cifications formelles.C'est dans ce contexte que s'inscrit cette thĂšse.La difficultĂ© principale d'une telle transformation rĂ©side dans l'ampleur du fossĂ© entre spĂ©cifications LN et spĂ©cifications formelles.L'objectif de mon travail de thĂšse est de proposer une approche permettant de vĂ©rifier automatiquement des spĂ©cifications d'exigences utilisateur, Ă©crites en langage naturel et dĂ©crivant le comportement d'un systĂšme.Pour cela, nous avons explorĂ© les possibilitĂ©s offertes par un modĂšle de reprĂ©sentation fondĂ© sur un formalisme logique.Nos contributions portent essentiellement sur trois propositions :1) une ontologie en OWL-DL fondĂ©e sur les logiques de description, comme modĂšle de reprĂ©sentation pivot permettant de faire le lien entre spĂ©cifications en langage naturel et spĂ©cifications formelles; 2) une approche d'instanciation du modĂšle de reprĂ©sentation pivot, fondĂ©e sur une analyse dirigĂ©e par la sĂ©mantique de l'ontologie, permettant de passer automatiquement des spĂ©cifications en langage naturel Ă leur reprĂ©sentation conceptuelle; et 3) une approche exploitant le formalisme logique de l'ontologie, pour permettre un passage automatique du modĂšle de reprĂ©sentation pivot vers un langage de spĂ©cifications formelles nommĂ© Maude.The main objective of system development is to address requirements. As such, success in its realisation is highly dependent on a requirement specification phase which aims to describe precisely and unambiguously all the characteristics of the system that should be developed. In order to arrive at a set of requirements, a user needs analysis is carried out which involves different parties (stakeholders). The system requirements are generally written in natural language to garantuee a wider understanding. However, since NL texts can contain semantic ambiguities, implicit information, or other inconsistenties, this can lead to diverse interpretations. Hence, it is not easy to specify a set of complete and consistent requirements, and therefore, the specified requirements must be formally checked. Specifications written in NL are not considered to be formal and do not allow for a direct application of formal methods. We must therefore transform NL requirements into formal specifications. The work presented in this thesis was carried out in this framework. The main difficulty of such transformation is the gap between NL requirements and formal specifications. The objective of this work is to propose an approach for an automatic verification of user requirements which are written in natural language and describe a system's expected behaviour. Our approach uses the potential offered by a representation model based on a logical formalism. Our contribution has three main aspects: 1) an OWL-DL ontology based on description logic, used as a pivot representation model that serves as a link between NL requirements to formal specifications; 2) an approach for the instantiation of the pivot ontology, which allows an automatic transformation of NL requirements to their conceptual representations; and 3) an approach exploiting the logical formalism of the ontology in order to automatically translate the ontology into a formal specification language called Maude.PARIS11-SCD-Bib. Ă©lectronique (914719901) / SudocSudocFranceF
ReprĂ©sentation et vĂ©riïŹcation dâun environnement intelligent Ă partir de spĂ©ciïŹcations utilisateur en langage naturel
International audienceAujourd'hui des capteurs et actionneurs associĂ©s Ă des pĂ©riphĂ©riques de contrĂŽle peuvent ĂȘtre installĂ©s n'importe oĂč, notamment dans nos maisons, crĂ©ant des environnements intelligents. Notre objectif est de permettre Ă un utilisateur de configurer son propre environne-ment intelligent en dĂ©crivant ses besoins, i.e. les rĂšgles de comportement de l'environnement, en langage naturel (LN). Nous explorons les possibilitĂ©s offertes par une ontologie formelle pour faire le lien entre spĂ©cifications en LN et spĂ©cifications formelles. L'analyse des spĂ©cifications LN permet l'instanciation automatique de l'ontologie afin qu'elle reprĂ©sente le comportement dĂ©crit par l'utilisateur. Les rĂšgles de comportement reprĂ©sentĂ©es sont alors traduites en spĂ©cifi-cations Maude, afin de complĂ©ter les vĂ©rifications possibles sous OWL. Nous montrons que tout au long de ce processus de formalisation, il est possible de vĂ©rifier la complĂ©tude, la cohĂ©rence et la conformitĂ© des exigences spĂ©cifiĂ©es et de maintenir une traçabilitĂ© entre spĂ©cification LN et spĂ©cifications formelles autorisant un retour prĂ©cis Ă l'utilisateur. ABSTRACT. Nowadays sensors and actuators associated with control devices can be installed anywhere, as in our homes creating smart environments. Our goal is to allow a user to configure her own smart environment by describing her needs, i.e. the environment behavioral rules, in natural language (NL). We explore the possibilities offered by an ontology, to transform NL specifications into formal specifications. Analysis of user requirements allows us an automatic instantiation of the ontology so that it represents the behavior described by the user. The represented behavioral rules are then translated into Maude specifications to complement ve-rifications realized in OWL. We show that throughout this formalization process, it is possible to check the completeness, the consistency and the conformity of the specified requirements and maintain traceability between NL requirements and formal specifications to allow a precise feedback to the user. MOTS-CLĂS : environnement intelligent, ontologie, spĂ©cifications, vĂ©rification formelle
Peuplement dâune ontologie guidĂ© par lâidentification dâinstances de propriĂ©tĂ©
National audienceDans le but de formaliser des spĂ©cifications dâexigence Ă©crites en langage naturel, nous avons choisi de modĂ©liser les connaissances du domaine par une ontologie et de reprĂ©senter formellement les spĂ©cifications par son peuplement. Lâapproche de peuplement est centrĂ©e sur lâidentification dâinstances de propriĂ©tĂ©s Ă partir des textes. Pour cela, des rĂšgles dâextraction sont acquises automatiquement Ă partir dâun corpus dâapprentissage, puis appliquĂ©es sur les textes pour lâidentification de mentions dâinstances de propriĂ©tĂ© reprĂ©sentĂ©es par des triplets. Ces rĂšgles exploitent les niveaux dâanalyse lexicale, syntaxique et sĂ©mantique et sont engendrĂ©es Ă partir des chemins syntaxiques rĂ©currents entre les termes pouvant dĂ©noter des instances de concept ou de propriĂ©tĂ©. Nous mon- trons que lâidentification dâinstances de pro- priĂ©tĂ©s permet dâidentifier de façon prĂ©cise les instances de concepts Ă©noncĂ©es de façon explicite ou implicite dans les textes
CrĂ©ation semi-automatique dâun corpus annotĂ© pour lâanalyse dâopinions
Nous dĂ©crivons une mĂ©thode semi-automatique pour la crĂ©ation dâun corpus annotĂ© en français. Ce corpus vise Ă permettre lâapprentissage dâun systĂšme dâanalyse dâopinions dans des textes portant sur lâĂ©valuation dâĂ©tablissements de recherche et dâenseignement supĂ©rieur. La crĂ©ation de ce corpus sâeffectue de maniĂšre itĂ©rative. Au cours de ces itĂ©rations une ontologie, une terminologie ainsi quâun ensemble de patrons syntaxico sĂ©mantiques sont crĂ©Ă©s automatiquement Ă partir dâannotations antĂ©rieures effectuĂ©es par des experts du domaine. Ces ressources permettent par la suite de guider lâannotation automatique de nouveaux corpus. Chaque corpus annotĂ© automatiquement est alors soumis Ă une nouvelle annotation manuelle des experts. Des rĂ©sultats empiriques montrent que notre mĂ©thode permet dâaccĂ©lĂ©rer et de faciliter le processus dâannotation. Le corpus rĂ©sultat est annotĂ© Ă la fois sĂ©mantiquement et syntaxiquement. Il est disponible gratuitement
From natural language specifications to formal specifications via an ontology as a pivot model
Le dĂ©veloppement d'un systĂšme a pour objectif de rĂ©pondre Ă des exigences. Aussi, le succĂšs de sa rĂ©alisation repose en grande partie sur la phase de spĂ©cification des exigences qui a pour vocation de dĂ©crire de maniĂšre prĂ©cise et non ambiguĂ« toutes les caractĂ©ristiques du systĂšme Ă dĂ©velopper.Les spĂ©cifications d'exigences sont le rĂ©sultat d'une analyse des besoins faisant intervenir diffĂ©rentes parties. Elles sont gĂ©nĂ©ralement rĂ©digĂ©es en langage naturel (LN) pour une plus large comprĂ©hension, ce qui peut mener Ă diverses interprĂ©tations, car les textes en LN peuvent contenir des ambiguĂŻtĂ©s sĂ©mantiques ou des informations implicites. Il n'est donc pas aisĂ© de spĂ©cifier un ensemble complet et cohĂ©rent d'exigences. D'oĂč la nĂ©cessitĂ© d'une vĂ©rification formelle des spĂ©cifications rĂ©sultats.Les spĂ©cifications LN ne sont pas considĂ©rĂ©es comme formelles et ne permettent pas l'application directe de mĂ©thodes vĂ©rification formelles.Ce constat mĂšne Ă la nĂ©cessitĂ© de transformer les spĂ©cifications LN en spĂ©cifications formelles.C'est dans ce contexte que s'inscrit cette thĂšse.La difficultĂ© principale d'une telle transformation rĂ©side dans l'ampleur du fossĂ© entre spĂ©cifications LN et spĂ©cifications formelles.L'objectif de mon travail de thĂšse est de proposer une approche permettant de vĂ©rifier automatiquement des spĂ©cifications d'exigences utilisateur, Ă©crites en langage naturel et dĂ©crivant le comportement d'un systĂšme.Pour cela, nous avons explorĂ© les possibilitĂ©s offertes par un modĂšle de reprĂ©sentation fondĂ© sur un formalisme logique.Nos contributions portent essentiellement sur trois propositions :1) une ontologie en OWL-DL fondĂ©e sur les logiques de description, comme modĂšle de reprĂ©sentation pivot permettant de faire le lien entre spĂ©cifications en langage naturel et spĂ©cifications formelles; 2) une approche d'instanciation du modĂšle de reprĂ©sentation pivot, fondĂ©e sur une analyse dirigĂ©e par la sĂ©mantique de l'ontologie, permettant de passer automatiquement des spĂ©cifications en langage naturel Ă leur reprĂ©sentation conceptuelle; et 3) une approche exploitant le formalisme logique de l'ontologie, pour permettre un passage automatique du modĂšle de reprĂ©sentation pivot vers un langage de spĂ©cifications formelles nommĂ© Maude.The main objective of system development is to address requirements. As such, success in its realisation is highly dependent on a requirement specification phase which aims to describe precisely and unambiguously all the characteristics of the system that should be developed. In order to arrive at a set of requirements, a user needs analysis is carried out which involves different parties (stakeholders). The system requirements are generally written in natural language to garantuee a wider understanding. However, since NL texts can contain semantic ambiguities, implicit information, or other inconsistenties, this can lead to diverse interpretations. Hence, it is not easy to specify a set of complete and consistent requirements, and therefore, the specified requirements must be formally checked. Specifications written in NL are not considered to be formal and do not allow for a direct application of formal methods. We must therefore transform NL requirements into formal specifications. The work presented in this thesis was carried out in this framework. The main difficulty of such transformation is the gap between NL requirements and formal specifications. The objective of this work is to propose an approach for an automatic verification of user requirements which are written in natural language and describe a system's expected behaviour. Our approach uses the potential offered by a representation model based on a logical formalism. Our contribution has three main aspects: 1) an OWL-DL ontology based on description logic, used as a pivot representation model that serves as a link between NL requirements to formal specifications; 2) an approach for the instantiation of the pivot ontology, which allows an automatic transformation of NL requirements to their conceptual representations; and 3) an approach exploiting the logical formalism of the ontology in order to automatically translate the ontology into a formal specification language called Maude
Des spécifications en langage naturel aux spécifications formelles via une ontologie comme modÚle pivot
The main objective of system development is to address requirements. As such, success in its realisation is highly dependent on a requirement specification phase which aims to describe precisely and unambiguously all the characteristics of the system that should be developed. In order to arrive at a set of requirements, a user needs analysis is carried out which involves different parties (stakeholders). The system requirements are generally written in natural language to garantuee a wider understanding. However, since NL texts can contain semantic ambiguities, implicit information, or other inconsistenties, this can lead to diverse interpretations. Hence, it is not easy to specify a set of complete and consistent requirements, and therefore, the specified requirements must be formally checked. Specifications written in NL are not considered to be formal and do not allow for a direct application of formal methods. We must therefore transform NL requirements into formal specifications. The work presented in this thesis was carried out in this framework. The main difficulty of such transformation is the gap between NL requirements and formal specifications. The objective of this work is to propose an approach for an automatic verification of user requirements which are written in natural language and describe a system's expected behaviour. Our approach uses the potential offered by a representation model based on a logical formalism. Our contribution has three main aspects: 1) an OWL-DL ontology based on description logic, used as a pivot representation model that serves as a link between NL requirements to formal specifications; 2) an approach for the instantiation of the pivot ontology, which allows an automatic transformation of NL requirements to their conceptual representations; and 3) an approach exploiting the logical formalism of the ontology in order to automatically translate the ontology into a formal specification language called Maude.Le dĂ©veloppement d'un systĂšme a pour objectif de rĂ©pondre Ă des exigences. Aussi, le succĂšs de sa rĂ©alisation repose en grande partie sur la phase de spĂ©cification des exigences qui a pour vocation de dĂ©crire de maniĂšre prĂ©cise et non ambiguĂ« toutes les caractĂ©ristiques du systĂšme Ă dĂ©velopper.Les spĂ©cifications d'exigences sont le rĂ©sultat d'une analyse des besoins faisant intervenir diffĂ©rentes parties. Elles sont gĂ©nĂ©ralement rĂ©digĂ©es en langage naturel (LN) pour une plus large comprĂ©hension, ce qui peut mener Ă diverses interprĂ©tations, car les textes en LN peuvent contenir des ambiguĂŻtĂ©s sĂ©mantiques ou des informations implicites. Il n'est donc pas aisĂ© de spĂ©cifier un ensemble complet et cohĂ©rent d'exigences. D'oĂč la nĂ©cessitĂ© d'une vĂ©rification formelle des spĂ©cifications rĂ©sultats.Les spĂ©cifications LN ne sont pas considĂ©rĂ©es comme formelles et ne permettent pas l'application directe de mĂ©thodes vĂ©rification formelles.Ce constat mĂšne Ă la nĂ©cessitĂ© de transformer les spĂ©cifications LN en spĂ©cifications formelles.C'est dans ce contexte que s'inscrit cette thĂšse.La difficultĂ© principale d'une telle transformation rĂ©side dans l'ampleur du fossĂ© entre spĂ©cifications LN et spĂ©cifications formelles.L'objectif de mon travail de thĂšse est de proposer une approche permettant de vĂ©rifier automatiquement des spĂ©cifications d'exigences utilisateur, Ă©crites en langage naturel et dĂ©crivant le comportement d'un systĂšme.Pour cela, nous avons explorĂ© les possibilitĂ©s offertes par un modĂšle de reprĂ©sentation fondĂ© sur un formalisme logique.Nos contributions portent essentiellement sur trois propositions :1) une ontologie en OWL-DL fondĂ©e sur les logiques de description, comme modĂšle de reprĂ©sentation pivot permettant de faire le lien entre spĂ©cifications en langage naturel et spĂ©cifications formelles; 2) une approche d'instanciation du modĂšle de reprĂ©sentation pivot, fondĂ©e sur une analyse dirigĂ©e par la sĂ©mantique de l'ontologie, permettant de passer automatiquement des spĂ©cifications en langage naturel Ă leur reprĂ©sentation conceptuelle; et 3) une approche exploitant le formalisme logique de l'ontologie, pour permettre un passage automatique du modĂšle de reprĂ©sentation pivot vers un langage de spĂ©cifications formelles nommĂ© Maude
ReadME generation from an OWL ontology describing NLP tools
International audienceThe paper deals with the generation of ReadME files from an ontology-based description of NLP tool. ReadME files are structured and organised according to properties defined in the ontology. One of the problem is being able to deal with multilingual generation of texts. To do so, we propose to map the ontol-ogy elements to multilingual knowledge defined in a SKOS ontology