Semantic and Knowledge Engineering Using ENVRI RM

dc.bibliographicCitation.firstPage100eng
dc.bibliographicCitation.lastPage119eng
dc.bibliographicCitation.volume12003eng
dc.contributor.authorMartin, Paul
dc.contributor.authorLiao, Xiaofeng
dc.contributor.authorMagagna, Barbara
dc.contributor.authorStocker, Markus
dc.contributor.authorZhao, Zhiming
dc.contributor.editorZhao, Zhiming
dc.contributor.editorHellström, Margareta
dc.date.accessioned2021-03-18T15:31:10Z
dc.date.available2021-03-18T15:31:10Z
dc.date.issued2020
dc.description.abstractThe ENVRI Reference Model provides architects and engineers with the means to describe the architecture and operational behaviour of environmental and Earth science research infrastructures (RIs) in a standardised way using the standard terminology. This terminology and the relationships between specific classes of concept can be used as the basis for the machine-actionable specification of RIs or RI subsystems. Open Information Linking for Environmental RIs (OIL-E) is a framework for capturing architectural and design knowledge about environmental and Earth science RIs intended to help harmonise vocabulary, promote collaboration and identify common standards and technologies across different research infrastructure initiatives. At its heart is an ontology derived from the ENVRI Reference Model. Using this ontology, RI descriptions can be published as linked data, allowing discovery, querying and comparison using established Semantic Web technologies. It can also be used as an upper ontology by which to connect descriptions of RI entities (whether they be datasets, equipment, processes, etc.) that use other, more specific terminologies. The ENVRI Knowledge Base uses OIL-E to capture information about environmental and Earth science RIs in the ENVRI community for query and comparison. The Knowledge Base can be used to identify the technologies and standards used for particular activities and services and as a basis for evaluating research infrastructure subsystems and behaviours against certain criteria, such as compliance with the FAIR data principles.eng
dc.description.versionpublishedVersioneng
dc.identifier.urihttps://oa.tib.eu/renate/handle/123456789/6096
dc.identifier.urihttps://doi.org/10.34657/5078
dc.language.isoengeng
dc.publisherCham : Springereng
dc.relation.doihttps://doi.org/10.1007/978-3-030-52829-4_6
dc.relation.essn1611-3349
dc.relation.isbn978-3-030-52828-7
dc.relation.ispartofTowards Interoperable Research Infrastructures for Environmental and Earth Scienceseng
dc.relation.ispartofseriesLecture Notes in Computer Science ; 12003eng
dc.relation.issn0302-9743
dc.rights.licenseCC BY 4.0 Unportedeng
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/eng
dc.subjectOntologyeng
dc.subjectKnowledge baseeng
dc.subjectResearch infrastructureeng
dc.subjectReference modeleng
dc.subject.ddc020eng
dc.titleSemantic and Knowledge Engineering Using ENVRI RMeng
dc.typebookParteng
dc.typeTexteng
dcterms.bibliographicCitation.journalTitleLecture Notes in Computer Scienceeng
tib.accessRightsopenAccesseng
wgl.contributorTIBeng
wgl.subjectErziehung, Schul- und Bildungsweseneng
wgl.typeBuchkapitel / Sammelwerksbeitrageng
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