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Ontology for Cloud Computing Instances. Instance are classes of VM that comprise varying combinations of CPU, memory, storage, and networking capacity. This ontology allows to define the instantiation model of MVs used in large cloud computing providers such as Amazon, Azure, etc. @en
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Ontology for the definition of regions and zones of availability on CloudComputing platforms and services. This ontology allows to define model of regions used in large cloud computing providers such as Amazon, Azure, etc. @en
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Service Level Agreement for Cloud Computing Services. This ontology allows to define model of SLA/SLO used in large cloud computing providers such as Amazon, Azure, etc., including terms, claims, credit, compensations, etc @en
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The Cochrane Core ontology describes the entities and concepts that exist in the domain of evidence based healthcare. It is used for the construction of the Cochrane Linked Data Vocabulary containing some 400k terms including Interventions (Drugs, Procedures etc), Populations (Age, Sex, Condition), and clinical Outcomes. @en
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The PICO ontology provides a machine accessible version of the PICO framework. It essentially provides a model for describing evidence in a consistent way. The model allows the specifying of complex populations, detailed interventions and their comparisons as well as the outcomes considered. The PICO ontology was originally designed to model the questions asked and answered in Cochrane's systematic reviews. As a leader in the field of evidence based healthcare Cochrane uses the PICO model when framing and publishing evidence based questions. The PICO model is widely adopted for describing healthcare evidence, furthermore is equally applicable in other evidence-based domains. It essentially provides a model for describing evidence in a consistent way. @en
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The ontology of the taxonomy "European Skills, Competences, qualifications and Occupations". The ontology considers three ESCO pillars (or taxonomy) and 2 registers. The three pillars are: - Occupation - Skill (and competences) - Qualification For the construction and use of the ESCO pillars, the following modelling artefacts are used: - Facetting support to specialize ESCO pillar concepts based on bussiness relevant Concept Groups (e.g. species, languages, ...) - Conept Groups, Thesaurus array and Compound terms (as detailed in ISO 25964) to organize faceted concepts - SKOS mapping properties to relate ESCO pillar concepts to concepts in other (external) taxonomies (e.g. FoET, ISCO88 and ISCO08. More mappings can be added in the future.) - Tagging ESCO pillar concepts by other (external) taxonomies (NUTS, EQF, NACE, ...) - Capture gender specifics on the labels of the ESCO pillar concepts - Rich ESCO concept relationships holding a description and other specific characteristics of the relation between two ESCO pillar concepts. ESCO maintains two additional registers: - Awarding Body - Work Context Awarding Bodies typically are referenced by ESCO qualifications. Occupations can have one or more work context. @en
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Ontology for public services and organizations @en
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Open 311 Ontology This ontology generalizes the concepts that appear in 311 open data files published by several cities (Toronto, New York, Chicago, Vancouver) across North America. It provides a generis representation of 311 data that other cities can map their data onto and be used as a means of achieving interoperability. @en
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IT Service Management Ontology (ITSMO) provides the vocabulary for annotating resources related to IT Service Management. ITSMO tries to be consistent with 2011 ITIL glossary. @en
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This ontology defines the most abstract concepts and properties that are needed to semantically manage resource within federated infrastructures @en
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This ontology defines concepts related to federation of internet infrastructures. @en
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The Ontology for Biomedical Investigations (OBI) is build in a collaborative, international effort and will serve as a resource for annotating biomedical investigations, including the study design, protocols and instrumentation used, the data generated and the types of analysis performed on the data. This ontology arose from the Functional Genomics Investigation Ontology (FuGO) and will contain both terms that are common to all biomedical investigations, including functional genomics investigations and those that are more domain specific. @en
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The Project Documents Ontology models the inherent structure and concepts of various documents in a project-specific setting, like meeting minutes, status reports etc. @en
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This vocabulary is intended to describe all the aspects which are needed to communicate incident related information for fire department services @en
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The AMLO-core is the main module of the AMLO projects that extends the Financial Industry Business Ontology (FIBO) with some concepts to describe the Anti Money Laundering (AML) knowledge and facts. @en