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An ontology to address the Research Management of the CRUE's Spanish University System (Sistema Universitario Español) by applying an encompassing model not only capable of addressing the universities of the CRUE but also more belonging to the European Union. @en
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The BBC ontology is used to describe BBC concepts in the store. For example, the BBC divisions (products) publishing linked data and interfacing with the triplestore, the platforms for which we produce content and the web documents that publish or are relevant to the BBC's content. @en
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A pattern for the description of scenarios that involve entities having some value during a particular time and within a particular context. @en
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This pattern is extracted from DOLCE-UltraLite by partial clone of elements and expansion. Two datatype properties have been added which allow to express the boundaries of the time interval. @en
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The NEPOMUK Calendaring Ontology intends to provide vocabulary for describing calendaring data (events, tasks, journal entries) which is an important part of the body of information usually stored on a desktop. It is an adaptation of the ICALTZD ontology created by the W3C RDF Calendar Task Force of the Semantic Web Interest Group in the Semantic Web Activity. @en
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This vocabulary defines temporal entities such as time intervals, their properties and relationships. @en
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A vocabulary to describe time zones and their geographical coverage. @en
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A vocabulary for description of events and calendars @en
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An entry sub-ontology of time (OWL-Time). @en
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ISTEX is a platform that aims to provide the entire French higher education and research community with an online access to retrospective collections of scientific literature in all disciplines. This unparalleled reservoir of multidisciplinary resources is complemented by a significant number of value-added services that can be used to optimise operations through content discovery and interactive valuation tools. @en
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Defines temporal / spatial concepts and general-purpose datastructures @en
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The ontology describes the main concepts in the field of education and the connections between them. The current version emphasizes the details of the study material, learning outcomes and the curriculum. @en
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A FAIR-compliant metadata vocabulary (MV), the Library Transaction Ontology (LTO), is designed and developed for representing and managing library transaction data (LTD), including circulation, interlibrary loan, hold requests, and fiscal information. Our investigation revealed that despite significant focus on bibliographic and authority data, no standardized vocabulary exists for LTD, resulting in data loss, mapping errors, and interoperability challenges during system migration and integration. LTO supports machine processing and automated reasoning for data-driven decision making. Additionally, it supports interoperability across heterogeneous library platforms by providing mappings for the circulation metadata of multiple Library Management Systems (LMS), including Koha, Virtua, Evergreen, SOUL, etc. It is also compliant with the Library of Congress's (LOC) BIBFRAME model and extends the bibliographic Linked Data (LD) framework to support the standardized representation of library transaction LD. LTO?s elements were validated through expert evaluation by LIS professionals, and its effectiveness in information processing and retrieval was successfully assessed. @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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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