Search results for "Web"

showing 10 items of 2018 documents

RDF2SPIN: Mapping Semantic Graphs to SPIN Model Checker

2011

International audience; The most frequently used language to represent the semantic graphs is the RDF (W3C standard for meta-modeling). The construction of semantic graphs is a source of numerous errors of interpretation. The processing of large semantic graphs is a limit to the use of semantics in current information systems. The work presented in this paper is part of a new research at the border between two areas: the semantic web and the model checking. For this, we developed a tool, RDF2SPIN, which converts RDF graphs into SPIN language. This conversion aims checking the semantic graphs with the model checker SPIN in order to verify the consistency of the data. To illustrate our propos…

[ INFO.INFO-MO ] Computer Science [cs]/Modeling and SimulationTheoretical computer science[INFO.INFO-WB] Computer Science [cs]/WebComputer science0211 other engineering and technologies[ INFO.INFO-WB ] Computer Science [cs]/WebTemporal logic02 engineering and technologyRDF/XMLRDF020204 information systemsSemantic computing021105 building & construction0202 electrical engineering electronic engineering information engineeringSPARQLBIMRDFCwmSemantic WebBIM.Semantic Web Rule Language[INFO.INFO-WB]Computer Science [cs]/WebModel-Checkingcomputer.file_format[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationSPINSemantic graphSemantic technologyIFC[INFO.INFO-MO] Computer Science [cs]/Modeling and Simulationcomputer
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Extending SPARQL with Temporal Logic

2009

The data integration and sharing activities carried on in the framework of the Semantic Web lead to large knowledge bases that must be queried, analyzed, and exploited efficiently. Many of the knowledge representation languages of the Semantic Web, starting with RDF, are based on directed, labeled graphs, which can be also manipulated using graph algorithms and tools coming from other domains. In this paper, we propose an analysis approach of RDF graphs by reusing the verification technology developed for concurrent systems. To this purpose, we define a translation from the SPARQL query language into XTL, a general-purpose graph manipulation language implemented in the CADP verification too…

[ INFO.INFO-MO ] Computer Science [cs]/Modeling and Simulation[INFO.INFO-LO] Computer Science [cs]/Logic in Computer Science [cs.LO][INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB][INFO.INFO-WB] Computer Science [cs]/Web[INFO.INFO-WB]Computer Science [cs]/Web[ INFO.INFO-WB ] Computer Science [cs]/WebInformationSystems_DATABASEMANAGEMENTlabeled transition system[INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO]ACM : H.: Information Systems/H.2: DATABASE MANAGEMENT/H.2.3: Languages/H.2.3.3: Query languagesSPARQL[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulationmodel checkingRDFACM: D.: Software/D.2: SOFTWARE ENGINEERING/D.2.4: Software/Program Verification/D.2.4.4: Model checking[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB]temporal logicACM : D.: Software/D.2: SOFTWARE ENGINEERING/D.2.4: Software/Program Verification/D.2.4.4: Model checking[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB][ INFO.INFO-LO ] Computer Science [cs]/Logic in Computer Science [cs.LO]ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT/H.2.3: Languages/H.2.3.3: Query languages[INFO.INFO-MO] Computer Science [cs]/Modeling and Simulationverification
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Qualifying semantic graphs using model checking

2011

International audience; Semantic interoperability problems have found their solutions using languages and techniques from the Semantic Web. The proliferation of ontologies and meta-information has improved the understanding of information and the relevance of search engine responses. However, the construction of semantic graphs is a source of numerous errors of interpretation or modeling and scalability remains a major problem. The processing of large semantic graphs is a limit to the use of semantics in current information systems. The work presented in this paper is part of a new research at the border of two areas: the semantic web and the model checking. This line of research concerns t…

[ INFO.INFO-MO ] Computer Science [cs]/Modeling and Simulation[INFO.INFO-WB] Computer Science [cs]/WebComputer science[ INFO.INFO-WB ] Computer Science [cs]/Web0102 computer and information sciences02 engineering and technologycomputer.software_genre01 natural sciencesSocial Semantic Webtemporal logicSemantic similaritySemantic computing0202 electrical engineering electronic engineering information engineeringSemantic analyticsSemantic integrationSemantic Web StackInformation retrievalbusiness.industry[INFO.INFO-WB]Computer Science [cs]/WebSemantic search020207 software engineeringSemantic interoperability[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationModel-checking010201 computation theory & mathematicsSemantic graphTheoryofComputation_LOGICSANDMEANINGSOFPROGRAMS[INFO.INFO-MO] Computer Science [cs]/Modeling and SimulationArtificial intelligencebusinesscomputerNatural language processing2011 International Conference on Innovations in Information Technology
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A new approach based on NμSMV Model to query semantic graph

2011

International audience; The language most frequently used to represent the semantic graphs is the RDF (W3C standard for meta-modeling). The construction of semantic graphs is a source of numerous errors of interpretation. Processing of large semantic graphs can be a limit to use semantics in modern information systems. The work presented in this paper is part of a new research at the border between two areas: the semantic web and the model checking. For this, we developed a tool, RDF2NμSMV, which converts RDF graphs into NμSMV language. This conversion aims checking the semantic graphs with the model checker NμSMV in order to verify the consistency of the data. The data integration and shar…

[ INFO.INFO-MO ] Computer Science [cs]/Modeling and Simulation[INFO.INFO-WB] Computer Science [cs]/WebComputer science[ INFO.INFO-WB ] Computer Science [cs]/WebNμSMVTemporal logic02 engineering and technologycomputer.software_genreQuery languageSPARQLtemporal logic queryRDFModel CheckingSemantic similarity020204 information systemsSemantic computing0202 electrical engineering electronic engineering information engineeringSPARQLRDFSemantic WebGraph databaseInformation retrieval[INFO.INFO-WB]Computer Science [cs]/Webcomputer.file_format[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationAbstract semantic graphSemantic graphQuery checking020201 artificial intelligence & image processing[INFO.INFO-MO] Computer Science [cs]/Modeling and Simulationcomputer
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Apports des réseaux sociaux pour la gestion de la relation client

2014

National audience; Depuis quelques années, le Web s'est transformé en une plateforme d'échanges. La gestion de relation client doit évoluer pour tirer partie des données disponibles sur les réseaux sociaux et mettre l'entreprise au coeur des échanges. Nous proposons dans cet article une approche générique de détection de communautés de clients d'une entreprise, basée sur leur comportement explicite et implicite, intégrant des données de sources diverses. Nous définissons une mesure de similarité, entre un utilisateur et un tag, prenant en compte la notation et la consultation des ressources et le réseau social de l'utilisateur. Nous validons cette approche sur une base exemple en utilisant …

[ INFO.INFO-SI ] Computer Science [cs]/Social and Information Networks [cs.SI]Web 2.0communautésrelation client[INFO.INFO-WB] Computer Science [cs]/Webgestion de la relation clientdécouverte de communautés[INFO.INFO-SI] Computer Science [cs]/Social and Information Networks [cs.SI][INFO.INFO-WB]Computer Science [cs]/Web[ INFO.INFO-WB ] Computer Science [cs]/Webtagsmodélisation d'utilisateurWeb 2.0.[INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]profilsréseaux sociauxSocial CRMInformation Systems
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A Context-Based Adaptation In Mobile Learning

2013

International audience; Recent developments on mobile devices and wireless technologies enable new technical capabilities for the learning domain. Nowadays, learners are able to learn anywhere and at any time. The dynamic and continually changing learning setting in learner's mobile environment gives rise to many different learning contexts. The challenge in context-aware mobile learning is to develop an approach building the best learning content according to dynamic learning situations. This paper aims to develop an adaptive system based on the semantic modeling of the learning content and the learning context. The behavioral part of this approach is made up of rules and metaheuristics to…

[ MATH.MATH-OC ] Mathematics [math]/Optimization and Control [math.OC][INFO.INFO-WB] Computer Science [cs]/Web[SHS.EDU]Humanities and Social Sciences/Education[SHS.EDU] Humanities and Social Sciences/Education[INFO.INFO-WB]Computer Science [cs]/Web[ INFO.INFO-WB ] Computer Science [cs]/Web[MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC][ SHS.EDU ] Humanities and Social Sciences/Education[ MATH.MATH-CO ] Mathematics [math]/Combinatorics [math.CO]context[MATH.MATH-CO] Mathematics [math]/Combinatorics [math.CO]mobile learning[INFO.INFO-MC]Computer Science [cs]/Mobile Computingsemantic web[INFO.INFO-MC] Computer Science [cs]/Mobile Computing[INFO.EIAH] Computer Science [cs]/Technology for Human Learning[ INFO.INFO-MC ] Computer Science [cs]/Mobile Computing[MATH.MATH-CO]Mathematics [math]/Combinatorics [math.CO][ INFO.EIAH ] Computer Science [cs]/Technology for Human Learning[INFO.EIAH]Computer Science [cs]/Technology for Human Learning[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC]Adaptation
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Un outil de mesure de l'audience d'un site Internet : l'analyse réseau

1999

Internet sales deeply change the traditional commercial techniques. To optimize the audience of its site, and more its sales on this media, an enterprise now needs relevant statistical information. Our research intends to renew the Log files analyzers, currently available tools, by showing the contributions of the network analysis.

[ SHS.INFO.BIBL ] Humanities and Social Sciences/Library and information sciences/domain_shs.info.biblanalyse réseauaudience[SHS.INFO]Humanities and Social Sciences/Library and information sciencessite web[SHS.INFO] Humanities and Social Sciences/Library and information sciences
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Analysing the mechanisms of common ground in collaborative web-based interaction

2001

The ideas presented in this paper are challenged especially by the certain critical questions concerning web-based interaction and the qualitative analysis of such interaction and learning. The question arises whether the students from different contexts and countries are able to reach such interaction that would lead them to educationally relevant higher-level discussion and learning in web-based environments. Furthermore, as this field of study is fairly novel, there is a shortage of established methodologies for analysing computer-mediated communication and the complex phenomena it encompasses. In this presentation, we attempt to find new approaches to discover how people establish and m…

[INFO.EIAH] Computer Science [cs]/Technology for Human Learninggroundingcommon groundcollaborative web-based interactionelectronic discussion
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A Use Case of Data Integration in Food Production

2018

International audience; This paper presents a use case about knowledge representation and integration of data from different domains in food science. An ontology named PO 2 DG, the Process and Observation Ontology for the production of Dairy Gels, has been designed in order to provide a shared vocabulary for domain experts. The available data have been semantically structured using PO 2 DG and are stored in an RDF repository named PO 2 DG dataset. This use case identifies some of the challenges when dealing with a multi domain representation problem, gives some hints about possible solutions and suggests some further work.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.3: Information Search and Retrievalexperimental observations represen- tationontology based data integrationprocess representation[INFO.INFO-WB] Computer Science [cs]/WebACM: H.: Information Systems[INFO.INFO-WB]Computer Science [cs]/Web[INFO]Computer Science [cs][INFO] Computer Science [cs]food science[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
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Semantic User Profiling for Digital Advertising

2015

International audience; With the emergence of real-time distribution of online advertising space (“real-time bidding”), user profiling from web navigation traces becomes crucial. Indeed, it allows online advertisers to target customers without interfering with their activities. Current techniques apply traditional methods as statistics and machine learning, but suffer from their limitations. As an answer, the proposed approach aims to develop and evaluate a semantic-based user profiling system for digital advertising.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Data AnalysisBig DataACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.5: Online Information Services[ INFO ] Computer Science [cs]OntologyACM : H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.1: Content Analysis and IndexingACM : H.: Information SystemsUser ProfilingACM: H.: Information Systems/H.4: INFORMATION SYSTEMS APPLICATIONSReasoningACM : H.: Information Systems/H.4: INFORMATION SYSTEMS APPLICATIONS[INFO] Computer Science [cs][INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]ACM : H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.4: Systems and Software/H.3.4.5: User profiles and alert servicesACM: H.: Information SystemsInferenceACM : H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.5: Online Information Services[INFO]Computer Science [cs]Logical Rules[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.1: Content Analysis and IndexingSWRLACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.4: Systems and Software/H.3.4.5: User profiles and alert servicesSemantic Web
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