131
results
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An ontology for describing programming language-specific runners, processors and pipelines in RDF-based data processing frameworks. @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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Smart Building Evacuation Ontology (SBEO) is an ontology that couples the information about any building with its occupants such that it can be used in many useful ways. For example, indoor localization of people, detection of any hazard, a recommendation of normal routes such as shopping or stadium seating routes, or safe and feasible emergency evacuation routes or both of them all together. The core SBEO covers the concepts related to the geometry of building, devices and components of the building, route graphs correspondent to the building topology, users' characteristics and preferences, situational awareness of both building (hazard detection, status of routes in terms of availability and occupancy) and users (tracking, management of groups, status in terms of fitness), and emergency evacuation. @en
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This ontology defines batteries and their state of charge ratio property. @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 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 Trading Ontology defines concepts and relations to describe ownership, trading, bilateral contracts and market licenses: - players own systems and trade commodities, which have a price; - bilateral electricity contracts are connections between electricity traders at which they exchange electricity; - electricity markets are connections between electricity traders at which they exchange electricity, using a market license; - electricity markets can be cleared, and balanced; - evaluations can have a traded volume validity context @en
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SemTS is an ontology designed to identify and describe segments within time series data, which are specific data points or intervals that can overlap. These segments encompass characteristic knowledge about the time interval they cover, including common time series features, structural anomalies, motifs, or information provided by domain experts. By classifying and semantically representing this knowledge, SemTS promotes organized reusability and efficient propagation, potentially reducing resource expenditure while enhancing future analyses. It employs established semantic approaches. Examples are DCAT to reference associated time series data, OWL-Time to define the index structure of time series data and segments or ML-Schema to expand the expressiveness regarding data analysis task information. SemTS's design involves categorizing time series knowledge and mapping it to specific intervals and dimensions of time series data. It introduces a class called TimeSeriesSegment to model these segments, extending the DCAT Dataset class to enable segments to be part of other segments. This structure allows for the association of knowledge, such as anomalies, with particular intervals or data points. TimeIndex specifications extend OWL-Time classes, while dimensional details are represented by DataDimension. The segment-wise consideration of knowledge indirectly serves as an index structure, linking meaningful time series data with categorized knowledge. At the highest level of abstraction, time series knowledge is divided into three categories: DataKnowledge, ScenarioKnowledge, and MethodKnowledge. DataKnowledge refers to insights extracted directly from the data or through analytical methods, such as class membership from time series clustering. ScenarioKnowledge describes verified contexts, including data annotations or domain-specific process knowledge, often equating to expert-provided a priori information and can also define facts derived from inferred knowledge. MethodKnowledge encompasses effective analytical method presets or mathematical/logical equivalents of established process information. @en
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A vocabulary specifying concepts and structures needed to represent different data cubes needed for the Smart Readiness Indicator. @en
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An ontology to describe people and requests for timebanks. This includes the poeple's skills, limitations, and environment. @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
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Module for dialogue process and system output management. @en
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Module for dialogue process and system output management. @en