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results
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This ontology defines a vocabulary for describing provenance traces of carbon emission calculations by capturing the quantifiable measurements of carbon emission sources used by some activities (e.g., electricity used by a machinery to produce a product, petrol used to make a car journey, etc.) and emission conversion factors used to estimate the carbon emissions produced by these. In addition, the ontology provides the ability to capture various data transformations that occurred before energy estimates may be used with relevant conversion factors. For example, sensors may provide raw readings about a water flow of an irrigation rig in an agri-food operation which is then used as a proxy to estimate the total volume of fertilisers used. @en
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The process execution ontology is a proposal for a simple extension of both the [W3C Semantic Sensor Network](https://www.w3.org/TR/vocab-ssn/) and the [Semantic Actuator Network](https://www.irit.fr/recherches/MELODI/ontologies/SAN.owl) ontology cores. @en
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The SEAS Operating Ontology defines evaluations of operating features of interest. @en
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This ontology defines proposed alignemnts with the QUDT ontology. @en
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This ontology defines common evaluation interpretation concepts for statistics. @en
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The System Ontology defines Systems, Connections between systems, and Connection Points at which systems may be connected. This ontology is then specialized for multiple domains. For example: - In electric energy: - power systems consume, produce, store, and exchange electricity; - power connections are where electricity flows between systems; - power connection points are plugs, sockets, or power busses. - In the electricity market: - players and markets are systems; - connections are contracts or transactions between two players, or between a player and a market; - connection points include offers and bids. @en
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This ontology defines: - a set of subclasses of `seas:Evaluation` to better interpret evaluations of quantifiable properties. - a set of sub properties of `seas:hasProperty` to qualify time-related properties. @en
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The SEAS Device ontology defines `seas:Device` as physical system that are designed to execute one or more procedures that involve the physical world. @en
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The SEAS Forecasting ontology extends the [Procedure Execution ontology (PEP)](https://w3id.org/pep/) @en
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This vocabulary is version v0.1 of the ITEA2 Smart Energy Aware Systems project vocabulary. It enables the description of electricity measurements of a site using the Data Cube W3C vocabulary. @en
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This ontology defines batteries and their state of charge ratio property. @en
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The SEAS Building ontology describes a taxonomy of buildings, building spaces, and rooms. Some categorizations are based on the energy efficiency related to their insulation etc., although the actual values for classes depend the country specific regulations and geographical locations. Other categorizations are based on occupancy and activities. There is no single accepted categorization available. This taxonomy uses some types selected from: - International building occupancy based categories (USA) - The Classification of Types of Constructions (EU) - Finnish building categorization VTJ2000 (Finland) - Wikipedia category page for Rooms: https://en.wikipedia.org/wiki/Category:Rooms @en
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The REACT ontology aims to represent all the necessary knowledge to support the achievement of island energy independence through renewable energy generation and storage, a demand response platform, and promoting user engagement in a local energy community. The REACT ontology has been developed as part of the REACT project which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 824395. @en
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With the aim of enhancing natural communication between workers in industrial environments and the systems to be used by them, TODO (Task-Oriented Dialogue management Ontology) has been developed to be the core of task-oriented dialogue systems. TODO is a core ontology that provides task-oriented dialogue systems with the necessary means to be capable of naturally interacting with workers (both at understanding and at ommunication levels) and that can be easily adapted to different industrial scenarios, reducing adaptation time and costs. Moreover, it allows to store and reproduce the dialogue process to be able to learn from new interactions. @en
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Module for Action (and Frame) modelling inside domain. @en