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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 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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An ontology to describe a Semantic-Web Machine Learning System (SWeMLS) @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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The vocabulary allows for the description of data about scientific events such as conferences, symposiums and workshops. @en
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ModSci is a reference ontology for modelling different types of modern sciences and related entities, such as scientific discoveries, renowned scientists, instruments, phenomena ... etc. @en
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Quality, architecture, and process are considered the keystones of software engineering. ISO defines them in three separate standards. However, their interaction has been poorly studied, so far. The SQuAP model (Software Quality, Architecture, Process) describes twenty-eight main factors that impact on software quality in banking systems, and each factor is described as a relation among some characteristics from the three ISO standards. Hence, SQuAP makes such relations emerge rigorously, although informally. SQaAP-Ont is an OWL ontology that formalises those relations in order to represent and reason via Linked Data about software engineering in a three-dimensional model consisting of quality, architecture, and process characteristics. @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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RDF-STaX is an OWL 2 DL ontology that enables describing the types of RDF streams and defines relations between them. @en
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The Simplified Upper Level Ontology (SULO) is ontology with a minimal set of classes and relations to guide the development of a personal health knowledge graph. @en
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The TIDO ontology can be used to describe the decision processes within the threat intelligence domain @en