Linked Open Vocabularies

dseco - The DNS Security Ontology

https://w3id.org/dseco/ontology/

The DNS Security Ontology (DSecO) project is a data model for representing and reasoning on Domain Name System (DNS) data. The ontology is developed using web technologies (e.g. RDF, OWL, SKOS) and is intended as a structure for realizing a DNS Knowledge Graph (KG) for administration and security assessment applications. The model has been developed in collaboration with operational teams, and in connection with third parties linked vocabularies. Alignment with third parties vocabularies is implemented on a per class or per property basis when relevant (e.g. with `rdfs:subClassOf`, `owl:equivalentClass`). Directions for direct instanciation of these vocabularies are provided for cases where implementing a class/property alignment is redundant. Alignment holds for the following vocabulary releases: - [ORG](https://www.w3.org/TR/vocab-org/) 0.8 - [UCO](https://github.com/ucoProject/uco) Release-0.8.0 @en

dsd - Description of a Data Source

https://w3id.org/dsd

To ensure comparability between schemas from different data models, the Description of a Data Source (DSD) vocabulary has been developed. @en

dg - DINGO Ontology

https://w3id.org/dingo/

The DINGO ontology (Data Integration for Grant Ontology) defines the terms of the DINGO vocabulary and provides a machine readable extensible framework to model data relative to projects, funding, project and funding actors, and, notably, funding policies. It is designed to yield high modeling power and elasticity to cope with the huge variety in funding and project practices, which makes it applicable to many areas where funding is an important aspect: first of all research, but also the arts, cultural conservation, and many others. @en

bdi - Belief-Desire-Intention Ontology

https://w3id.org/fossr/ontology/bdi/

The BDI Ontology provides a formal framework to model the Belief-Desire-Intention (BDI) architecture for rational agents. It defines key mental states—Beliefs, Desires, and Intentions—and their relationships, capturing the agent’s reasoning, motivation, and commitment to action. Supporting classes include Propositions (content of mental states), Justifications (rationale for mental states), Plans (action sequences for goals), and TimeIntervals (temporal validity of entities). Key properties like hasBelief, hasDesire, and hasIntention link agents to mental states, while fulfills, adoptsIntention, and motivatesDesire model dynamic interactions. Temporal properties enable reasoning about time-sensitive states and plans. Axioms ensure consistency, such as disjointness between mental states and domain-specific constraints. This ontology supports reasoning, querying, and analysis of agent behaviour, enabling applications in AI, multi-agent systems, and decision support. @en

aml - AutomationML Ontology

https://w3id.org/i40/aml

A vocabulary to represent the AutomationML Standard - IEC 62714 @en

iddo - The Interconnected Data Dictionary Ontology (IDDO)

https://w3id.org/iddo

The interconnected data dictionary ontology maps the data model of the ISO 23386 for the describing, creating, and maintenance of properties in interconnected data dictionaries. @en

ids - IDS Information Model

https://w3id.org/idsa/core

This ontology defines classes and properties for describing participants, infrastructure, data and services of the International Data Spaces (formerly known as Industrial Data Space). @en

authn_provider - Authentication Provider

https://w3id.org/inrupt/namespace/vocab/authn_provider/

Information about authentication providers which might be identity providers or other services such as ones providing JSON Web Tokens. @en

lto - Library Transaction Ontology

https://w3id.org/lto

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

mdo-calc - Materials Design Ontology - Calculation Module

https://w3id.org/mdo/calculation/

This is the calculation module of Materials Design Ontology. @en

mdo - Materials Design Ontology - Full

https://w3id.org/mdo/full/

This is the Materials Design Ontology. @en

mdo-struc - Materials Design Ontology - Structure Module

https://w3id.org/mdo/structure/

This is the structure module in Materials Design Ontology. @en

noria - The NORIA Ontology

https://w3id.org/noria/ontology/

The NORIA-O project is a data model for IT networks, events and operations information. The ontology is developed using web technologies (e.g. RDF, OWL, SKOS) and is intended as a structure for realizing an IT Service Management (ITSM) Knowledge Graph (KG) for Anomaly Detection (AD) and Risk Management applications. The model has been developed in collaboration with operational teams, and in connection with third parties linked vocabularies. Alignment with third parties vocabularies is implemented on a per class or per property basis when relevant (e.g. with `rdfs:subClassOf`, `owl:equivalentClass`). Directions for direct instanciation of these vocabularies are provided for cases where implementing a class/property alignment is redundant. Alignment holds for the following vocabulary releases: - [BBO](https://hal.archives-ouvertes.fr/hal-02365012/) 1.0.0 - [BOT](https://w3id.org/bot/) 0.3.2 - [DevOps-Infra](https://oeg-upm.github.io/devops-infra/) 1.0.0 - [FOLIO](https://github.com/IBCNServices/Folio-Ontology) 1.0.0 - [ORG](https://www.w3.org/TR/vocab-org/) 0.8 - [PEP](https://w3id.org/pep/) 1.1 - [SEAS](https://w3id.org/seas/) 1.1 - [SLOGERT](https://sepses.ifs.tuwien.ac.at/ns/log/index-en.html) 1.1.0 - [UCO](https://github.com/ucoProject/uco) Release-0.8.0 @en

sdom - The Software Description Ontology for Models

https://w3id.org/okn/o/sdm

The Software Description Ontology for Models (SDM) expands the software description ontology (SD) to add information about scientific software models. Examples of scientific software models are hydrology models, agriculture models or economy models. @en

nno - The Neural Network Ontology

https://w3id.org/nno/ontology

This is the Neural Network Ontology. Designed by the AIFB (http://www.aifb.kit.edu/web/Web_Science) @en

Patterns?:	type name both
Flatten?:	yes no

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