890
results
-
The SEAS Forecasting ontology extends the [Procedure Execution ontology (PEP)](https://w3id.org/pep/) @en
-
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
-
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
-
A security ontology to annotate resources with security-related information @en
-
The SEM Ontology defines entities that make up the context of an event: Events, Actors, Places, Times. @en
-
A vocabulary to describe signs in a semiotic approach @en
-
semiotics- A content ontology pattern that encodes a basic semiotic theory, by reusing the situation pattern.A content ontology pattern that encodes a basic semiotic theory, by reusing the situation pattern. The basic classes are: Expression, Meaning, Reference (the semiotic triangle), LinguisticAct (for the pragmatics), and Agent. A linguistic act is said to be context for expressions, with their meanings and references, and agents involved. Based on this pattern, several specific linguistic acts, such as 'tagging', 'translating', 'defining', 'formalizing', etc. can be defined, so constituting a formal vocabulary for a pragmatic web. @en
-
SemSur, the Semantic Survey Ontology, is a core ontology for describing individual research problems, approaches, implementations and evaluations in a structured, comparable way. @en
-
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
-
The vocabulary allows for the description of data about scientific events such as conferences, symposiums and workshops. @en
-
Intended to represent sequence schemas. It defines the notion of transitive and intransitive precedence and their inverses. It can then be used between tasks, processes, time intervals, spatially locate objects, situations, etc. @en
-
Ontology that defines core concepts for representing YANG servers, including connection details and the available YANG datastores, along with operations for retrieving YANG data from a YANG server. The goal of this ontology is to enable the declarative and abstract of the interactions with YANG servers for monitoring and configuration purposes. In this sense, the ontology can become the basis for building a knowledge graph from YANG data obtained from YANG servers. @en
-
A micro-ontology that defines the general concept of a service. @en
-
A specification of GeoSPARQL for simple features geometries (points, lines, polygons ...) @en
-
This vocabulary defines terms used in SHACL, the W3C Shapes Constraint Language. @en