Trained Language Model

This note provides some resources that are either duplicates or an extention of the PCTWebizenUseOfOntology notes made also.

In the webizen ecosystem; one of the objectives is to seek to create top-level ontology that is sought to be fit-for-purpose for applications relating to human beings (self); then sociosphere / sociology and biosphere ontologies. in combination this may be considered an approach that seeks to distinguish our consciousness and our natural world; as something other than another persons 'thing'. Then, whilst there are many ontologies to describe things, new ontologies may be created using natural language; which would in-turn also be defined in a way that creates meaningful relationships between existing ontologies and PCTOntologyModelling outputs.

There are some existing Vocab Models that are defined using RDF/OWL.

Any that are not listed below will be be added to this NLP google-drive repo that also stores info about NLP related docs and notes (although there may be more elsewhere)

Mathematics Ontology

The use of these works, whilst sought to be designed to support Human Centric principals (including Human Centric AI principals); will end-up being processed by a software agent (on a computer). Perhaps therefore defining in the 'upper ontology' mathematics may in-turn improve support for 'comprehensible sense making' or in otherwords, inferencing, etc.

LINKS: https://github.com/CLLKazan/MathSearch https://github.com/CLLKazan/OntoMathPro https://github.com/CLLKazan/OntoMathPro/tree/develop

Pre:Existing Large Language Models.

Whilst investigating solutions, an array of existing language models have been identified that provide a great deal of the underlying data that is considered to be required, although the methods to employ best employ them is presently unclear.

Whilst making a note of the work done previously making enquiries with ChatGPT as is illustrated by: ChatGPTDynamicOntology and in-turn the VocabularyModelling folder has been created to 'create space' for more thougher investigation. In-order to illustrate the considerations; i'll start with illustrating the resources that i've found so far.

Framenet: https://framenet.icsi.berkeley.edu/fndrupal/ https://github.com/chanind/frame-semantic-transformer https://github.com/topics/framenet

https://github.com/dbamman/latin-bert http://wordnet-rdf.princeton.edu/

https://en.wikipedia.org/wiki/Cyc https://old.datahub.io/dataset/opencyc is unavailable; a version of it has been found: https://github.com/asanchez75/opencyc/blob/master/opencyc-latest.owl.gz

https://www.ontologyportal.org/ https://github.com/ontologyportal/sumo

OntoWordNet LINKS https://lists.w3.org/Archives/Public/public-swbp-wg/2005Feb/0066.html https://www.w3.org/2001/sw/BestPractices/WNET/

Categories

Trained Language Model

Mathematics Ontology

Pre:Existing Large Language Models.