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  • 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
  • msr - Measurement Ontology
    http://www.telegraphis.net/ontology/measurement/measurement#
    The Measurement Ontology is an ontology in which measurements may be rendered @en
  • xhv - XHTML Vocabulary
    http://www.w3.org/1999/xhtml/vocab
    This is a vocabulary collection utilized by XHTML Family modules and document types using XHTML Modularization, including XHTML Role and XHTML + RDFa as defined in rdfa-syntax. @en
  • wdrs - Protocol for Web Description Resources
    http://www.w3.org/2007/05/powder-s
    The Protocol for Web Description Resources (POWDER) allows metadata to be associated with groups of resources such as those found on a Web site. @en
  • ldp - Linked Data Platform
    http://www.w3.org/ns/ldp#
    A set of best practices and simple approach for a read-write Linked Data architecture, based on HTTP access to web resources that describe their state using the RDF data model. @en
  • acl - Basic Access Control ontology
    http://www.w3.org/ns/auth/acl
    Defines the element of Authorization and its essential properties, and also some classes of access such as read and write. @en
  • http - HTTP in RDF
    http://www.w3.org/2011/http
    A namespace for describing HTTP messages (http://www.w3.org/Protocols/rfc2616/rfc2616.html) @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
  • wl - WSMO-Lite Ontology
    http://www.wsmo.org/ns/wsmo-lite#
    WSMO-Lite is a lightweight approach to the semantic annotation of Web service descriptions, defined by the STI2 working group Conceptual Models for Services @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
  • pubsub - Eccenca Publish-Subscribe Vocabulary
    https://vocab.eccenca.com/pubsub/
    The eccenca Publish-Subscribe Vocabulary defines concepts and relations to create statements about publishers, subscribers and their subscriptions in a Publish-Subscribe environment based on the PubSubHubbub Core 0.4 specification. @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