Search results for "Retrieval"

showing 10 items of 1176 documents

Towards a methodology for semantic and context-aware mobile learning

2013

International audience; Internet and mobile devices open the way towards mobile learning (m-learning), offering new opportunities to extend learning beyond the traditional teacher-led classroom. M-learning is not only any form of teaching or studying that takes place when the user interacts with a mobile device. It is more than just using a mobile device to access resources and communicate with others. It should take account of the constant mobile situation of the learner. The challenge here is to exploit this continually changing situation with a system that can dynamically recognize and adapt educational resources and services to the "context" in which the learner operates (localization, …

[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR][INFO.INFO-WB] Computer Science [cs]/WebExploitComputer science[ INFO.INFO-IU ] Computer Science [cs]/Ubiquitous Computing[ INFO.INFO-WB ] Computer Science [cs]/WebContext (language use)02 engineering and technologycontextWorld Wide Webmobile learning[INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing[INFO.INFO-MC]Computer Science [cs]/Mobile ComputingConstant (computer programming)semantic web[INFO.INFO-MC] Computer Science [cs]/Mobile Computing[ INFO.INFO-MC ] Computer Science [cs]/Mobile Computing0202 electrical engineering electronic engineering information engineeringMobile searchSemantic Webbusiness.industry[INFO.INFO-WB]Computer Science [cs]/Web[INFO.INFO-IU] Computer Science [cs]/Ubiquitous Computing020206 networking & telecommunicationsOpen learning[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]The Internet[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]businessMobile device
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Semantic aware RSS query algebra

2010

International audience; Existing XML query algebras are not fully appropriate to retrieve RSS news items mainly due to three reasons: 1) RSS is text rich and its content is dependent on the wording and verbification of the author, thus semantic aware operators are needed; 2) news items are dynamic and consequently time oriented retrieval is needed; 3) a news item may evolve through time, or overlap with other news items and hence identifying relationships between items is also needed. In this paper, we aim to solve these issues by providing a dedicated RSS algebra based on semantic-aware operators that consider RSS characteristics. The provided operators are application domain specific and …

[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR][INFO.INFO-WB] Computer Science [cs]/Webcomputer.internet_protocolComputer scienceRSS[ INFO.INFO-WB ] Computer Science [cs]/Web[SCCO.COMP]Cognitive science/Computer science02 engineering and technologyQuery optimizationQuery algebraQuery expansion[SCCO.COMP] Cognitive science/Computer scienceApplication domain020204 information systems0202 electrical engineering electronic engineering information engineering[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]Equivalence (formal languages)[ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM]Semantic queryInformation retrieval[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB][INFO.INFO-WB]Computer Science [cs]/Web[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]computer.file_format[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB][INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR][ SCCO.COMP ] Cognitive science/Computer science020201 artificial intelligence & image processing[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]computerXML
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Reasoning with Vague Spatial Information from Upper Mesopotamia (2000BC)

2015

International audience; Concepts such as near, far, south of, etc., are by its own nature vague. However, they are quite common in human language. In the case of historical records, these concepts are often the only source of information regarding the position of ancient places whose exact location has been lost. In our research, we use digitized written records from Upper Mesopotamia (2000BC) from the HIGEOMES project. Our goal is to provide better understanding of the location of places, based on the analysis of spatial statements. In our approach, we analyse cardinal statements between places with known location. Using this information we construct a probabilistic function representing t…

[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR]media_common.quotation_subjectReasonning02 engineering and technology[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Description logic0202 electrical engineering electronic engineering information engineeringMesopotamia ;[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]Function (engineering)Spatial analysisGeneral Environmental ScienceMathematicsmedia_commondescription logicsInformation retrievalPoint (typography)Ontologybusiness.industryProbabilistic logic[INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO]020207 software engineeringVaguenessspatial uncertainty[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]Upper MesopotamiaOntologyGeneral Earth and Planetary Sciences[ INFO.INFO-LO ] Computer Science [cs]/Logic in Computer Science [cs.LO]020201 artificial intelligence & image processingArtificial intelligencebusinessConstruct (philosophy)Procedia Environmental Sciences
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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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Saliency in spectral images

2011

International audience; Even though the study of saliency for color images has been thoroughly investigated in the past, very little attention has been given to datasets that cannot be displayed on traditional computer screens such as spectral images. Nevertheless, more than a means to predict human gaze, the study of saliency primarily allows for measuring infor- mative content. Thus, we propose a novel approach for the computation of saliency maps for spectral images. Based on the Itti model, it in- volves the extraction of both spatial and spectral features, suitable for high dimensionality images. As an application, we present a comparison framework to evaluate how dimensionality reduct…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputer scienceComputation0211 other engineering and technologiesComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingImage (mathematics)[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingInformative content0202 electrical engineering electronic engineering information engineeringVisual attentionComputer visionRelevance (information retrieval)spectral images[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing021101 geological & geomatics engineeringSaliencybusiness.industryDimensionality reductionPattern recognitionKadir–Brady saliency detector020201 artificial intelligence & image processingArtificial intelligenceHigh dimensionalitybusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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Ontology-driven Image Analysis for Histopathological Images

2010

International audience; Ontology-based software and image processing engine must cooperate in new fields of computer vision like microscopy acquisition wherein the amount of data, concepts and processing to be handled must be properly controlled. Within our own platform, we need to extract biological objects of interest in huge size and high-content microscopy images. In addition to specific low-level image analysis procedures, we used knowledge formalization tools and high-level reasoning ability of ontology-based software. This methodology made it possible to improve the expressiveness of the clinical models, the usability of the platform for the pathologist and the sensitivity or sensibi…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputer science[INFO.INFO-IM] Computer Science [cs]/Medical ImagingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage processingOntology (information science)[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]030218 nuclear medicine & medical imaging03 medical and health sciences[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]0302 clinical medicineSoftware[STAT.AP] Statistics [stat]/Applications [stat.AP][INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingDigital image processing[ INFO.INFO-TI ] Computer Science [cs]/Image Processing[INFO.INFO-IM]Computer Science [cs]/Medical ImagingComputer visionRDFImage analysis[STAT.AP]Statistics [stat]/Applications [stat.AP]Information retrieval[ INFO.INFO-IM ] Computer Science [cs]/Medical Imagingbusiness.industry[ STAT.AP ] Statistics [stat]/Applications [stat.AP][INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Usabilitycomputer.file_formatAutomatic image annotation[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV]030220 oncology & carcinogenesis[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]Artificial intelligencebusinesscomputer
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Cross-Media Color Reproduction and Display Characterization

2012

International audience; In this chapter, we present the problem of cross-media color reproduction, that is, how to achieve consistent reproduction of images in different media with different technologies. Of particular relevance for the color image processing community is displays, whose color properties have not been extensively covered in previous literature. Therefore, we go more in depth concerning how to model displays in order to achieve colorimetric consistency. The structure of this chapter is as follows: After a short introduction, we introduce the field of cross-media color reproduction, including a brief description of current standards for color management, the concept of colori…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processingbusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONColor reproductionCross media[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technologyBiologyColor management01 natural sciencesCharacterization (materials science)law.invention010309 opticsConsistency (database systems)[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processinglaw0103 physical sciences0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingColor image processingRelevance (information retrieval)Computer visionArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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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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