Search results for "Semantic"

showing 10 items of 941 documents

Incorporating depth information into few-shot semantic segmentation

2021

International audience; Few-shot segmentation presents a significant challengefor semantic scene understanding under limited supervision.Namely, this task targets at generalizing the segmentationability of the model to new categories given a few samples.In order to obtain complete scene information, we extend theRGB-centric methods to take advantage of complementary depthinformation. In this paper, we propose a two-stream deep neuralnetwork based on metric learning. Our method, known as RDNet,learns class-specific prototype representations within RGB anddepth embedding spaces, respectively. The learned prototypesprovide effective semantic guidance on the corresponding RGBand depth query ima…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Artificial neural networkComputer sciencebusiness.industry[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]020206 networking & telecommunications02 engineering and technologyImage segmentationSemanticsVisualization[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingMetric (mathematics)0202 electrical engineering electronic engineering information engineeringEmbeddingRGB color modelSegmentationComputer visionArtificial intelligencebusiness
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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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TOWARDS SEMANTIC INTEROPERABILITY FOR ENTERPRISE INFORMATION SYSTEMS

2016

International audience; In the context of globalisation, data and knowledge management, and given the need to deliver a quick response to changes in market forces, enterprises have to collaborate using information technologies to succeed in a disparate and dynamical business environment. Enterprise interoperability (EI) is a field of study that aims to improve this collaboration. Moreover, EI addresses problems related to the lack of system interoperability in organisations. In this paper, we focus on approaches which deliver semantic interoperability among enterprise information systems. We conclude by identifying the main drawbacks of such approaches to be adopted by world-wide industry a…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Enterprise InteroperabilityModel-driven Interoperability (MDI)ontology[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]semantic interoperability[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
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FOWLA, A Federated Architecture for Ontologies.

2015

International audience; The progress of information and communication technologies has greatly increased the quantity of data to process. Thus, managing data heterogeneity is a problem nowadays. In the 1980s, the concept of a Federated Database Architecture (FDBA) was introduced as a collection of components to unite loosely coupled federation. Semantic web technologies mitigate the data heterogeneity problem, however due to the data structure heterogeneity the integration of several ontologies is still a complex task. For tackling this problem, we propose a loosely coupled federated ontology architecture (FOWLA). Our approach allows the coexistence of various ontologies sharing common data…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Federated Ontology ArchitectureComputer scienceProcess (engineering)Distributed computing[INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS]Ontology (information science)SPARQL[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL][INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]World Wide WebSPARQLArchitecture[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]Semantic WebComputingMilieux_MISCELLANEOUSSWRLOWLHorn-like rules[INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO]computer.file_formatSemantic interoperabilityData structuresemantic interoperabilitybackward-chaining reasoningInformation and Communications Technologycomputer
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Enhancing scientific information systems with semantic annotations

2013

International audience; Scientific Information Systems aim to produce or improve knowledge on a subject through activities of research and development. The management of scientific dat a requires some essential properties. We propose SemLab an architecture that sup ports interoperability, data quality and extensibility through a unique paradigm: semantic annotation. We present two app lications that validate our architecture.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Information retrieval[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB][INFO.INFO-WB] Computer Science [cs]/WebComputer scienceInteroperability[INFO.INFO-WB]Computer Science [cs]/Web[ INFO.INFO-WB ] Computer Science [cs]/WebSubject (documents)02 engineering and technologyOntology (information science)Semantic interoperabilityData science[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI][ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB]020204 information systemsData qualitySemantic computing0202 electrical engineering electronic engineering information engineeringInformation system[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]020201 artificial intelligence & image processing[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]
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How to Enrich Description Logics with Fuzziness

2017

International audience; The paper describes the relation between fuzzy and non-fuzzy description logics. It gives an overview about current research in these areas and describes the difference between tasks for description logics and fuzzy logics. The paper also deals with the transformation properties of description logics to fuzzy logics and backwards. While the process of transformation from a description logic to a fuzzy logic is a trivial inclusion, the other way of reducing information from fuzzy logic to description logic is a difficult task, that will be topic of future work.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Theoretical computer science[ INFO ] Computer Science [cs]Relation (database)Process (engineering)Computer scienceMathematics::General Mathematics0102 computer and information sciences02 engineering and technology[INFO] Computer Science [cs]01 natural sciencesFuzzy logicTask (project management)[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Knowledge-based systemsFuzzy Description LogicDescription logicComputer Science::Logic in Computer Science0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]Semantic WebSemantic WebUncertaintyTransformation (function)TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES010201 computation theory & mathematics020201 artificial intelligence & image processingComputingMethodologies_GENERALHardware_LOGICDESIGN
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Customizing Semantic Profiling for Digital Advertising

2014

International audience; Personalization is the new magic buzzword of application development. To make the complexity of today's application functionalities and information spaces "digestible", customization has become the new go-to technique. But while those technologies aim to ease the consumption of media for their users, they suffer from the same problematic: in the age of Big Data, applications have to cope with a conundrum of heterogeneous information sources that have to be perceived, processed and interpreted. Researchers tend to aim for a maximum degree of integration to create the perfect, all-embracing personalization. The results are wide-range, but overly complex systems that su…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][ INFO ] Computer Science [cs]Computer scienceBig dataComplex systemsemantic technologies02 engineering and technology[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]Personalization[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]World Wide Web020204 information systems0202 electrical engineering electronic engineering information engineeringProfiling (information science)Heterogeneous information[ INFO.INFO-CL ] Computer Science [cs]/Computation and Language [cs.CL][INFO]Computer Science [cs]user profiles[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]OWLuser profilingbusiness.industryScalabilitySemantic technology020201 artificial intelligence & image processingbusinessDigital advertisingcustomization
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Multi-Domain Retrieval of Geospatial Data Sources Implementing a Semantic Catalogue

2015

International audience; Nowadays, the expertise of a user plays an important role in the search and retrieval in the information systems that usually combines general and specialized knowledge in the construction of queries. In addition, most of the queries systems are currently restricted on specific domains. Tackling these issues, we propose a methodology that implements a semantic catalogue in order to provide a smart queries system for retrieving data sources on the web by means of the extension of the user expertise. We propose the combination of a query expansion method, and the use of similarity measures and controlled vocabularies. Thus, it allows the system to recommend data source…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR][ INFO.INFO-TT ] Computer Science [cs]/Document and Text Processing[INFO.INFO-LO] Computer Science [cs]/Logic in Computer Science [cs.LO][INFO.INFO-WB] Computer Science [cs]/Web[INFO.INFO-WB]Computer Science [cs]/Websimilarity across ontologies.[ INFO.INFO-WB ] Computer Science [cs]/Web[INFO.INFO-TT] Computer Science [cs]/Document and Text Processing[INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO]multi-domain retrievalsmart queries[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]: Semantic catalogue[INFO.INFO-TT]Computer Science [cs]/Document and Text Processing[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR][ INFO.INFO-LO ] Computer Science [cs]/Logic in Computer Science [cs.LO][INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]knowledge engineering
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Integration of Spatial Technologies and Semantic Web Technologies for Industrial Archaeology

2010

International audience; We propose a method that uses the advancement in spatial technologies from current database systems within the Semantic Web Technologies in order to enrich and to populate the knowledge of a domain defined in an OWL-DL ontology. The results of spatial operations and functions are used to populate and to enrich ontologies with new individuals and new relationships. The advantage of spatial analysis within Semantic Web technologies is the diversity of the functionalities provided by the combination of spatial operations and the rule language of the Semantic Web (SWRL). This method is applied in the industrial archaeology domain in order to enhance the knowledge managem…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB][ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB]semantic webspatial analysisindustrial archaeology[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]ontologyknowledge management[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]SWRL[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]OWL
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Graphe-Based Rules For XML Data Conversion to OWL Ontology

2010

International audience; The paper presents a flexible method to enrich and populate an existing OWL ontology from XML data based on graph-based rules. These rules are defined in order to populate automatically a new version of an OWL ontology. Today, most of data exchanged between information systems is done with the help of the XML document. Leading researches in the domain of database systems are moving to semantic model in order to store data and its semantics definition. This flexible method consists in populating an existing OWL ontology from XML data. In paper we present such a method based on the definition of a graph which represents rules that drive the populating process.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]ontology enrichment[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB]XML dataComputingMethodologies_DOCUMENTANDTEXTPROCESSING[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]Ontology population[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]OWL ontologysemantic annotation[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]RDF
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