0000000000178652

AUTHOR

Arturs Sprogis

showing 6 related works from this author

Pini Language and PiniTree Ontology Editor: Annotation and Verbalisation for Atomised Journalism

2020

We present a new ontology language Pini and the PiniTree ontology editor supporting it. Despite Pini language bearing lot of similarities with RDF, UML class diagrams, Property Graphs and their frontends like Google Knowledge Graph and Protege, it is a more expressive language enabling FrameNet-style natural language annotation for Atomised journalism use case.

Computer science05 social sciences050801 communication & media studies02 engineering and technologycomputer.file_formatOntology languageProtégéLinguisticsAnnotation0508 media and communicationsUnified Modeling Language0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingJournalismClass diagramRDFcomputerNatural languagecomputer.programming_language
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Domain-Specific OWL Ontology Visualization with OWLGrEd

2015

The OWLGrEd ontology editor allows graphical visualization and authoring of OWL 2.0 ontologies using a compact yet intuitive presentation that combines UML class diagram notation with textual Manchester syntax for expressions. We present an extension mechanism for OWLGrEd that allows adding custom information areas, rules and visual effects to the ontology presentation thus enabling domain specific OWL ontology visualizations. The usage of OWLGrEd and its extensions is demonstrated on ontology engineering examples involving custom annotation visualizations, advanced UML class dia-gram constructs and integrity constraints in semantic database schema design.

Computer sciencecomputer.internet_protocolProgramming languageOntology-based data integrationProcess ontologySuggested Upper Merged OntologyOntology (information science)computer.software_genreOntology engineeringOWL-SWorld Wide WebOpen Biomedical OntologiesUpper ontologycomputer
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Metamodel specialization based DSL for DL lifecycle data management

2020

A new Domain Specific Language (DSL) based approach to Deep Learning (DL) lifecycle data management (LDM) is presented: a very simple but universal DL LDM tool, still usable in practice (called Core tool); and an advanced extension mechanism, that converts the Core tool into a DSL tool building framework for DL LDM tasks. The method used is based on the metamodel specialisation approach for DSL modeling tools introduced by authors.

Domain-specific languageSIMPLE (military communications protocol)business.industryComputer scienceData managementDeep learning020207 software engineering02 engineering and technologyUSableMetamodelingDigital subscriber lineSoftware_SOFTWAREENGINEERINGSpecialization (logic)0202 electrical engineering electronic engineering information engineeringArtificial intelligencebusinessSoftware engineeringProceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings
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Towards DSL for DL Lifecycle Data Management

2020

A new method based on Domain Specific Language (DSL) approach to Deep Learning (DL) lifecycle data management tool support is presented: a very simple DL lifecycle data management tool, which however is usable in practice (it will be called Core tool) and a very advanced extension mechanism which in fact converts the Core tool into domain specific tool (DSL tool) building framework for DL lifecycle data management tasks. The extension mechanism will be based on the metamodel specialization approach to DSL modeling tools introduced by authors. The main idea of metamodel specialization is that we, at first, define the Universal Metamodel (UMM) for a domain and then for each use case define a …

Domain-specific languagebusiness.industryComputer scienceData managementDeep learningUSableDomain (software engineering)MetamodelingDigital subscriber lineSoftware_SOFTWAREENGINEERINGSpecialization (functional)Artificial intelligencebusinessSoftware engineering
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Implementing a Face Recognition System for Media Companies

2018

During the past few years face recognition technologies have greatly benefited from the huge progress in machine learning and now have achieved precision rates that are even comparable with humans. This allows us to apply face recognition technologies more effectively for a number of practical problems in various businesses like media monitoring, security, advertising, entertainment that we previously were not able to do due to low precision rates of existing face recognition technologies. In this paper we discuss how to build a face recognition system for media companies and share our experience gained from implementing one for Latvian national news agency LETA. Our contribution is: which …

EntertainmentComputer scienceMedia monitoringAgency (sociology)languageLatvianFacial recognition systemData scienceImplementationlanguage.human_language
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Text Extraction from Scrolling News Tickers

2020

While a lot of work exists on text or keyword extraction from videos, not a lot can be found on the exact problem of extracting continuous text from scrolling tickers. In this work a novel Tesseract OCR based pipeline is proposed for location and continuous text extraction from scrolling tickers in videos. The solution worked faster than real time, and achieved a character accuracy of 97.3% on 45 min of manually transcribed 360p videos of popular Latvian news shows.

Information retrievalComputer scienceCharacter (computing)ScrollingExtraction (chemistry)ComputingMethodologies_DOCUMENTANDTEXTPROCESSINGKeyword extractionTesseractPipeline (software)
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