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situ - Situation Pattern http://www.ontologydesignpatterns.org/cp/owl/situation.owl A pattern to represent contexts or situations, and the things that are contextualized. @en -
tis - Time Indexed Situation http://www.ontologydesignpatterns.org/cp/owl/timeindexedsituation.owl A generic pattern usable for all situations that require a temporal indexing. @en -
np - Nano publication ontology http://www.nanopub.org/nschema The nanopub ontology @en -
tisc - Open Time and Space Core Vocabulary http://www.observedchange.com/tisc/ns# TISC, the Open Time and Space Core Vocabulary, is a lightweight spatiotemporal vocabulary aiming to provide spatial and temporal terms such as "happensAt", "locatedAt", "rightOf" to enable practitioners to relate their data to time and space. @en -
sealit - SeaLiT Ontology http://www.sealitproject.eu/ontology/ The SeaLiT Ontology is a formal ontology intended to facilitate the integration, mediation and interchange of heterogeneous information related to maritime history. It aims at providing the semantic definitions needed to transform disparate, localised information sources of maritime history into a coherent global resource. It also serves as a common language for domain experts and IT developers to formulate requirements and to agree on system functionalities with respect to the correct handling of historical information. The ontology uses and extends the CIDOC Conceptual Reference Model (ISO 21127:2014), in particular version 7.1.1, as a general ontology of human activity, things and events happening in space and time. @en -
opmw - The OPMW Ontology http://www.opmw.org/ontology/ OPMW is a OPMV profile to model the executions and definitions of scientific workflows. @en -
msr - Measurement Ontology http://www.telegraphis.net/ontology/measurement/measurement# The Measurement Ontology is an ontology in which measurements may be rendered @en -
mls - Machine Learning Schema http://www.w3.org/ns/mls ML-Schema is a collaborative, community effort with a mission to develop, maintain, and promote standard schemas for data mining and machine learning algorithms, datasets, and experiments @en -
ssn - Semantic Sensor Network Ontology http://www.w3.org/ns/ssn/ This ontology describes sensors, actuators and observations, and related concepts. It does not describe domain concepts, time, locations, etc. these are intended to be included from other ontologies via OWL imports. @en -
istex - Istex ontology for scholarly documents and extracted entities https://data.istex.fr/ontology/istex# 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 -
tfo - Transformation Functions Ontology https://privatealpha.com/ontology/transformation/1# This document describes functions which transform HTTP representations, i.e., the actual literal payloads of HTTP messages. @en -
ce - CityExplorer Ontology https://purl.org/cityexplorer This ontology models personalized tourist experiences by representing cities, points of interest, events, accommodations, restaurants, transportation, and their relationships. This ontology is part of a university project. @en -
edifact-o - EDIFACT Ontology https://purl.org/edifact/ontology An Ontology for representing EDIFACT Messages. @en -
edu - Education Ontology https://schema.edu.ee/ 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 -
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