Mostly a fully automatic translation through deepl - I started to think about this in german...
Or Reify vs. RDF Star. Or Davidsonian event semantics for modeling.
I think I got the above example with Caesar from John Sowa's "Knowledge Representation", that I unfortunately lent and never got back, so the exact page reference is missing.
Yesterday I was at the Connected Data Meetup in London, and listened to a presentation advocating taxonomies as starting points for ontology creation, by Madi Weland Solomon.
She didn't discuss this at length, so the following is my understanding, all errors and bad ideas are mine :)
When I read the article "Knowledge Graphs: Opportunities and Challenges" this morning, I thought again about the relationship between symbolic AI, say, in the form of the Semantic Web, i.e. RDF and OWL, and statistical AI in the form of neural networks, language models and transformers.
ChatGPT shows how far statistical AI has come, here we can clearly see approaches to general intelligence, AGI.
Business knowledge blueprints are a powerful tool for identifying the basic structure and requirements of a business. By working with a coach or facilitator to create a business model blueprint, organizations can gain a clearer understanding of their business model and how it fits into the broader market.
RDF, OWL, and SHACL are powerful formalisms that can be used to express and analyze the results of a business knowledge blueprint coaching or workshop. These formalisms allow businesses to represent and manipulate data in a structured and meaningful way, enabling them to make more informed decisions about their business model and strategy.
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