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The Building Concrete Monitoring Ontology (BCOM) is defined for capturing information of concrete work, concrete curing and testing of concrete properties. Further Information on the development and usage of the Ontology can be found in the following publication: Liu et al. (2021): An ontology integrating as-built information for infrastructure asset management using BIM and semantic web. In: Proceedings of 2021 European Conference on Computing in Construction, Online eConference, URL: https://ec-3.org/publications/conferences/2021/paper/?id=167 @en
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The Building Topology Ontology (BOT) is a simple ontology defining the core concepts of a building. It is a simple, easy to extend ontology for the construction industry to document and exchange building data on the web. Changes since version 0.2.0 of the ontology are documented in: https://w3id.org/bot/bot.html#changes The version 0.2.0 of the ontology is documented in: Mads Holten Rasmussen, Pieter Pauwels, Maxime Lefrançois, Georg Ferdinand Schneider, Christian Anker Hviid and Jan Karlshøj (2017) Recent changes in the Building Topology Ontology, 5th Linked Data in Architecture and Construction Workshop (LDAC2017), November 13-15, 2017, Dijon, France, https://www.researchgate.net/publication/320631574_Recent_changes_in_the_Building_Topology_Ontology The initial version 0.1.0 of the ontology was documented in: Mads Holten Rasmussen, Pieter Pauwels, Christian Anker Hviid and Jan Karlshøj (2017) Proposing a Central AEC Ontology That Allows for Domain Specific Extensions, Lean and Computing in Construction Congress (LC3): Volume I – Proceedings of the Joint Conference on Computing in Construction (JC3), July 4-7, 2017, Heraklion, Greece, pp. 237-244 https://doi.org/10.24928/JC3-2017/0153 @en
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The Construction Dataset Context (CDC) ontology is an extension of DCAT v2.0, a W3C Recommendation ontology for describing (RDF and non-RDF) datasets published on the Web. Using this extension, it becomes possible to describe a context for construction-related datasets that are being distributed using Web technology as well as datasets that are not shared outside an organization such as local copies, work in progress and other datasets that remain internal. This dataset metadata encompasses the temporal context (period or snapshot), the type of content of the dataset (as-built, design, etc.) and relations between contextualized datasets (previous as-built, requirements related to a design, etc.). In addition, this DCAT extension also provides terminology for managing dataset distributions that are scoped to a certain (named or default) graph of an RDF file or quadstore. @en
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Simple ontology for Cloud Computing Services. This ontology allows to define model of prices used in large cloud computing providers such as Google, Amazon, Azure, etc., including options for regions, type of instances, prices specification, etc. @en
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An ontology containing additional terminology for structuring and annotating RDFS/OWL taxonomies for describing constructions (components, materials, spatial zones, damages, construction tasks and properties). It also functions as an index for known taxonomies starting from root classes and properties. @en
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The Construction Tasks Ontology (CTO) describes tasks operating on construction elements, spatial zones and/or damages. The tasks are either planned or executed depending on the dataset metadata context of the dataset its used in. Five different types of tasks are defined: instalment, removal, modification, repair and inspection. Consequences of tasks on the dataset, i.e. added and/or deleted triples, are modeled using reified statements. The tasks can link to a reified statement using the CTO relations. @en
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Ontology that defines the topology of damages in constructions. @en
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To ensure comparability between schemas from different data models, the Description of a Data Source (DSD) vocabulary has been developed. @en
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The DNS Security Ontology (DSecO) project is a data model for representing and reasoning on Domain Name System (DNS) data. The ontology is developed using web technologies (e.g. RDF, OWL, SKOS) and is intended as a structure for realizing a DNS Knowledge Graph (KG) for administration and security assessment applications. The model has been developed in collaboration with operational teams, and in connection with third parties linked vocabularies. Alignment with third parties vocabularies is implemented on a per class or per property basis when relevant (e.g. with `rdfs:subClassOf`, `owl:equivalentClass`). Directions for direct instanciation of these vocabularies are provided for cases where implementing a class/property alignment is redundant. Alignment holds for the following vocabulary releases: - [ORG](https://www.w3.org/TR/vocab-org/) 0.8 - [UCO](https://github.com/ucoProject/uco) Release-0.8.0 @en
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An ontology that describes the management of the traffic in a straight road with two lanes, both in the same direction. @en
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SAREF4INMA is an extension of SAREF for the industry and manufacturing domain. SAREF4INMA focuses on extending SAREF for the industry and manufacturing domain to solve the lack of interoperability between various types of production equipment that produce items in a factory and, once outside the factory, between different organizations in the value chain to uniquely track back the produced items to the corresponding production equipment, batches, material and precise time in which they were manufactured. SAREF4INMA is specified and published by ETSI in the TS 103 410-5 associated to this ontology file. SAREF4INMA was created to be aligned with related initiatives in the smart industry and manufacturing domain in terms of modelling and standardization, such as the Reference Architecture Model for Industry 4.0 (RAMI), which combines several standards used by the various national initiatives in Europe that support digitalization in manufacturing. The full list of use cases, standards and requirements that guided the creation of SAREF4INMA are described in the associated ETSI TR 103 507. @en
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The PPROC ontology defines the necessary concepts to describe public procurement process and the contracts of public sector (public e-procurement). The ontology has been designed with the main purpose of publishing data about public contracts. @en
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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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Simple and direct pricing ontology for Cloud Computing Services. This ontology allows to define model of prices used in large cloud computing providers such as Amazon, Azure, etc., including options for regions, type of instances, prices specification, 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