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cem - Crime Event Model (CEM) https://w3id.org/CEMontology The Crime Event Model is an ontology for the representation of crime events extracted from local newspapers. It could be employed for Crime Analysis purposes: extracting crime information from newspapers and enriching them with proper machine-readable semantics is a critical task to help law enforcement agencies at preventing crime, supporting criminal investigations and evaluating the action of law enforcement agencies themselves. The model is based on the fundamental 5W1H journalistic questions, that are Who?, What?, When?, Where?, Why? and How?. Another important requirement was the attempt to exploit existing knowledge graphs and ontologies such as the Simple Event Model (SEM) Ontology and the Schema.org data model for interoperability and interconnection. @en -
gdprov - The GDPR Provenance ontology https://w3id.org/GDPRov GDPRov is an OWL2 ontology to express provenance metadata of consent and data lifecycles towards documenting compliance for GDPR. @en -
airo - AI Risk Ontology https://w3id.org/airo AIRO represents AI risk concepts and relations based on the AI Act draft and ISO 31000 standard series. @en -
citedcat - CiteDCAT-AP Vocabulary https://w3id.org/citedcat-ap CiteDCAT-AP is an extension of the DCAT application profile for data portals in Europe (DCAT-AP) for describing resources documented by using the DataCite metadata schema - the de facto standard for data citation, and used across scientific disciplines. Its basic use case is to make research data searchable on general data portals, thereby bridging the gap between scientific and public sector information. For this purpose, CiteDCAT-AP provides an RDF vocabulary and the corresponding RDF syntax binding for the metadata elements defined in DataCite. @en -
cmd - Compound Measure Description https://w3id.org/cmd# This document is a vocabulary to describe compound measures, i.e. measures with several metric or item that are organized with serveral dimensions. The description of such a measure relies on a Tree-Structure of Requirement (TSoR): a set of requirements structured hierarchicaly with analysis element. A TSoR represents the main measure. Several information may be added to explicitely indicate how the overall score on the measure should be calculated based on the hierarchy, relative importance of the node of the hierarchy and an aggregation function. The measure can be described completely and unambiguously from the organisation to the requirements and the implementation. @en -
dpv - Data Privacy Vocabulary (DPV) https://w3id.org/dpv The Data Privacy Vocabulary (DPV) provides terms (classes and properties) to represent information about processing of personal data, for example - purposes, processing operations, personal data, technical and organisational measures. @en -
dio - The Design Intent Ontology https://w3id.org/dio The scope of the DIO is the domain of design intent or design rationale that needs to be documented while undertaking the design of any artifact @en -
eeo - Experimental Evaluation Ontology https://w3id.org/eeo An ontology to describe experiments, evaluations and their relation. @en -
fairo - FAIR-O: An Ontology for FAIR Assessment and Scoring https://w3id.org/fair-o The FAIR Assessment Ontology is designed to model the process and outcomes of evaluating digital objects according to the FAIR principles. It captures FAIR assessments as provenance-aware activities, linking evaluated resources to detailed results for each FAIR sub-principle. Each assessment can include multiple sub-principle results, associated evidence, quantitative scores, and test outcomes (e.g., pass, fail, indeterminate). The ontology also models the computational aspects of FAIR evaluation, including scoring functions, calculation algorithms, and aggregation methods used to derive higher-level scores. The ontology is aligned with the FAIR Vocabulary (https://w3id.org/fair/principles/terms/) for the definition of principles and sub-principles, and reuses established standards such as PROV-O for provenance and SKOS for controlled vocabularies. This ensures interoperability with existing FAIR tools and supports integration into automated FAIR assessment pipelines. @en -
chameo - CHAracterisation MEthodology Ontology https://w3id.org/emmo/domain/characterisation-methodology/chameo CHAMEO is a domain ontology designed to model the common aspects across the different characterisation techniques and methodologies. @en -
puv - Parameter Usage Vocabulary ontology https://w3id.org/env/puv A simple ontology which implements the Parameter Usage Vocabulary semantic model, as described at https://github.com/nvs-vocabs/P01 @en -
eppl - The EP-Plan ontology https://w3id.org/ep-plan PROV extension for linking Plans and parts of plans to their respective executions. @en -
icon - ICON Ontology https://w3id.org/icon/ontology/ The ICON ontology deals with high granularity art interpretation. It was developed by conceptualizing Panofsky's theory of levels of interpretation, therefore artworks can be described according to Pre-iconographical, Iconographical and Iconological information. @en -
ishi - Ishikawa diagram ontology https://w3id.org/ishikawa-diagram-ontology The Ishikawa ontology aims to provide a data and view model to manage data encoded in Ishikawa diagrams which are also known as fishbone or cause and effect diagram (CED). Ishikawa diagrams result from (iterative) workshops. Thus, the ontology includes the basic modelling of workshops to create Ishikawa diagrams. @en -
loin - Level of Information Need (LOIN) Ontology https://w3id.org/loin The Level of Information Need (LOIN) Ontology is defined for specifying information requirements for delivery of data in a buildings' life cycle. The LOIN ontology is based on the standard BS EN 17412-1 (2020). Furthermore, it is extended with vocabulary for connecing Information Delivery Specifications (IDS) and Information containers for linked document delivery (ICDD) as per ISO 21597-1 (2020). @en