SemWebOntologies
Ontologies are defined using RDF.
The Development Folder for ontology related work is currently located here on github
The Objective is to work to ensure the ontologies refer to working URIs. Some new ontology work may thereby be required, with the appropriate work to define the relations with pre-existing / historical ontologies / URIs.
Part of what is sought to be achieved, is to decentralise the storage of URIs on protocols that are less suceptable to dead-links via DecentralisedOntologies efforts, which is in-turn part of the PermissiveCommons objectives more broadly.
CORE Semantic Web Ontologies include;
A list of many others in the github/linkeddata/ontology-archiver (approx count 2205); and,
Facebook: https://ogp.me/ Search: https://schema.org/
Lod Cloud: https://lod-cloud.net/ WikiData: https://www.wikidata.org/wiki/Wikidata:Main_Page DbPedia: https://www.dbpedia.org/
Prefix.CC search for ontologies ( https://prefix.cc/ )
solid ontologies: https://solidproject.org/developers/vocabularies/well-known/common
The work on ontologies is interactive with the work on PermissiveCommonsTech.
The most significant area of work that is being produced (somewhat slowly atm) is about ValuesCredentials which is basically about the ability to create VerifiableCredentials to support SafetyProtocols, CommunityOfPractice, CommercialModels and HumanCentricDigitalIdentity. What this means in practice, is that the existing well known values frameworks that exist (ie: UN instruments, Professional Conduct outlines, etc.); need to be authored into a linked-data format so that they can be used with verifiable credentials, which can in-turn be employed in relation to agreements made between agents (ie: people and/or people and entities or groups).
Some ontologies are no-longer available at the original location that is defined in the ontology document. Considerations (options analysis) is underway, to figure out what to do about those sorts of cases. In other cases, the ontology frameworks are not appropriate and/or outdated.
Ontology Modelling can include processing ontologies to have inferred comprehension in relation to terms that are defined using a different ontology.
The greatest of all examples; is that in-effect, a persons life may be described as an ontology.
Some resources have been provided to help people better UnderstandingOntologies from a social perspective. Therein, the way SemanticWeb technologies work, is not simply about software agents; but moreover, about the relationships between software agents, and humanity. There are several qualities of ontological design that are important for technologists to consider when designing human-centric AI systems: OpenAI offers the following considerations...
- Inclusivity: The ontology should be inclusive and representative of diverse perspectives and experiences. This can help to ensure that the AI system is fair and unbiased, and that it is able to effectively serve the needs of a diverse user base.
- Precision: The ontology should be precise and unambiguous, using clear definitions and relationships between concepts. This can help to reduce confusion and misunderstandings, and improve the accuracy of the AI system.
- Completeness: The ontology should be complete, covering all relevant concepts and relationships within the domain. This can help to ensure that the AI system has a thorough understanding of the domain, and is able to make informed decisions and recommendations.
- Flexibility: The ontology should be flexible and adaptable, allowing for changes and updates as new knowledge is acquired or the domain evolves. This can help to ensure that the AI system remains current and relevant over time.
- Human-centeredness: The ontology should be designed with a human-centered approach, taking into account the needs and preferences of users. This can help to make the AI system more intuitive and user-friendly.
- Transparency: The ontology should be transparent, clearly documenting the concepts and relationships that are being represented. This can help to ensure that the AI system is understandable and explainable, and that users can trust and have confidence in its decisions and recommendations.
- Interoperability: The ontology should be designed to be interoperable with other systems and standards, allowing for easy integration and communication with other systems. This can help to increase the reach and impact of the AI system, and enable it to work effectively within a broader ecosystem.
- Scalability: The ontology should be scalable, able to handle a large volume of data and concepts without losing performance or accuracy. This can help to ensure that the AI system can effectively serve the needs of a growing user base.
- Sustainability: The ontology should be designed with sustainability in mind, taking into account the long-term maintenance and evolution of the AI system. This can help to ensure that the system is able to continue to serve the needs of users over time.
Overall, it is important for technologists to carefully consider the qualities of ontological design when designing human-centric AI systems, as this can have a significant impact on the effectiveness and usability of the system. This is in-turn broadly known as WebScience.