Description of concept "Events Metadata"RDF feed Events Metadata [[Category:Event]] [[Academic Field::Academic Field:Metadata]]
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vocabularies for the publication of statistical metadata (code lists and classifications, descriptive metadata, provenance and quality information, etc.)?
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concept "Event Series Metadata"RDF feed Event Series Metadata [[Category:Event Series]] [[Academic Field::Academic Field:Metadata]]
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concept "Events Metadata And Semantics"RDF feed Events Metadata And Semantics [[Category:Event]] [[Academic Field::Academic Field:Metadata And Semantics]]
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Series Metadata And Semantics"RDF feed Event Series Metadata And Semantics [[Category:Event Series]] [[Academic Field::Academic Field:Metadata And Semantics]]
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"Events Metadata And Semantic Research"RDF feed Events Metadata And Semantic Research [[Category:Event]] [[Academic Field::Academic Field:Metadata And Semantic
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bibliotēka City Riga Region Rīga Country Latvia (LV) Academic Field Archiving, Metadata, Digital Preservation Official Website https://www.imaging.org/site/IS
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Date 2020-05-21 Status as scheduled Mode online Academic Field Archiving, Metadata, Digital Preservation Official Website https://www.imaging.org/IST/Con
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Description of concept "Event Series Metadata And Semantic Research"RDF feed Event Series Metadata And Semantic Research [[Category:Event Series]] [[Academic
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Columbia Country United States of America (US) Academic Field Archiving, Metadata, Digital Preservation Official Website https://www.imaging.org/IST/IST
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City Lisabon Region Lisboa Country Portugal (PT) Academic Field Archiving, Metadata, Digital Preservation Official Website https://www.imaging.org/IST/Con
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entity descriptions. So, we distinguish here schema data, instance data, and metadata. What is important is that they are fully compliant to FAIR Data Principles
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Interpretability in modelling * Kernel methods * Knowledge discovery in databases * Metadata and the design of statistical data bases * Model uncertainty, multiple
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(multilingual) LRs, ontologies and Semantic Web technologies �?�Metadata descriptions of LRs and metadata for semantic/content markup Exploitation of LRs in different
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(multilingual) LRs, ontologies and Semantic Web technologies • Metadata descriptions of LRs and metadata for semantic/content markup • Validation, quality assurance
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matching, merging, mediation and reconciliation) Ontology Learning and Metadata Generation (e.g., HLT and ML approaches) Multimedia and Semantic Web Semantic
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cross-language retrieval, structured documents, language-specific IR, metadata-based IR, social tagging for IR, music IR, speech IR, image IR, video IR
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scientific databases and workflows * Visualization of complex scientific data * Metadata management * Storage, querying, and analysis of sensor streams * Privacy
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Event Acronym MMUI 2008 Title Metadata Mining for Image Understanding Start Date 2008-01-21 End Date 2008-01-21 Status as scheduled Mode on site City Funchal
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Ontologies, Paraphrasing, Dynamic Modeling, Verification, Consistency Checking, Metadata Harvesting * Natural Language in Software Engineering: Software design
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