58
results
-
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
-
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
-
The notion of territory plays a major role in human and social sciences. In an historical context, most approaches are irrelevant as they rely on geometric data, which is not available. In order to represent historical territories,we conceived the HHT ontology (Hierarchical Historical Territory) to represent hierarchical historical territorial divisions, without having to know their geometry. This approach relies on a notion of building blocks to replace polygonal geometry @en
-
The Cultural Event module models cultural events, i.e. events involving cultural properties. @en
-
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
-
An RDF vocabulary to describe and facilitate the usage of a Multidimensional Interface. @en
-
This ontology defines feature of interest and their properties, as an extension of the core classes of the SSN ontology (https://www.w3.org/ns/ssn/). A feature of interest is an abstraction of a real world phenomena (thing, person, event, etc). A feature of interest is then defined in terms of its properties, which are qualifiable, quantifiable, observable or operable qualities of the feature of interest. Alignments to other ontologies are proposed in external documents: - [SSNAlignment](https://w3id.org/seas/SSNAlignment) proposes an alignment to the [SSN ontology](http://www.w3.org/ns/ssn/). - [QUDTAlignment](https://w3id.org/seas/QUDTAlignment) proposes an alignment to the [QUDT ontology](http://qudt.org/). @en
-
The Procedural Knowledge Ontology (PKO) addresses the Procedural Knowledge (PK) domain, and models procedures, their executions, and related resources and agents. @en
-
The vocabulary allows for the description of data about scientific events such as conferences, symposiums and workshops. @en
-
Ontology 'Usability' created to describe and store information about interactions of user with a software user interface @en
-
Ontology for representing exceptions to reporting of parents, for entities registered with a Legal Entity Identifier. The Global Legal Identifier System (GLEIS) requires that legal entities with an LEI provide information on their ultimate and direct accounting consolidating parents. Relationship reporting is mandatory with exceptions allowed for certain well-defined reasons. This ontology provides a simple structure for recording reasons for each exception by LEI. @en
-
RiC-O (Records in Contexts-Ontology) is an OWL ontology for describing archival record resources. As the second part of Records in Contexts standard, it is a formal representation of Records in Contexts Conceptual Model (RiC-CM). The current official version is <html:strong>v0.2</html:strong>; it is compliant with RiC-CM v0.2 full draft, that will be published in February or March 2021, and that is slightly different from <html:a href="https://www.ica.org/sites/default/files/ric-cm-0.2_preview.pdf">RiC-CM v0.2 preview, that was published in December 2019. RiC-O provides a generic vocabulary and formal rules for creating RDF datasets (or generating them from existing archival metadata) that describe in a consistent way any kind of archival record resource. It can support publishing RDF datasets as Linked Data, querying them using SPARQL, and making inferences using the logic of the ontology. @en
-
Ontology designed to provide an RDF representation of Hypermedia Controls, in particular links and forms. @en