Today I listened to the workshop "Democratize the Knowledge Graph and Concrete Tooling Requirements" by Adam Keresztes at the 2023 Knowledge Graph Conference in NYC.
Different people there emphasized the difficulty of explaining the difference between classes and instances to non-ontologists.
Which had me reflecting on how I work with this terminology, and I found that I tend to use 'set' instead of 'class'.
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.
I was reminded last week at a conference of the Gesellschaft für Wissensmanagement e.V. Knowledge Management in the 21st Centurythat learning is also defined as change in behavior.
To quote Wikipedia:
A recent client project has again brought to my attention two important benefits of W3C standards around semantic technologies and graph data:
Why RDF? Because it protects your investment!
Our company is proud to have a diverse and talented team of partners, each with their own areas of expertise and specialization.
Markus Graf is a security specialist and software developer with a focus on the HR field. He is knowledgeable in HR processes, graph databases, and Clojure, and has a strong understanding of internet security.
Axel Timo Purr is a writer, journalist, and film critic with a specialization in African culture and politics, as well as children's and youth cinema.
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Markus Anderwald is also a designer with a focus on user experience and interface design.
Together, our partners bring a wealth of knowledge and experience to our company, and we are grateful to have them as part of our team.
Our company offers a range of services focused on knowledge graph, linked data, business analysis, and coaching.
Our knowledge graph and linked data services include the design and implementation of data models using technologies like RDF, OWL, and SHACL. We can help businesses to create flexible and powerful data models that can be easily analyzed and understood, using logic and semantics to structure and organize the data.
Our business analysis services include the identification of business opportunities, the improvement of existing business models, and the development of strategic plans. We use tools like business model blueprints and formalisms like RDF, OWL, and SHACL to create clear and structured representations of business data that can be easily analyzed and understood.
We also offer coaching services to help businesses and individuals develop new skills and knowledge. Our coaching services are tailored to meet the specific needs and goals of our clients, and can include everything from technical training to teambuilding and leadership development.
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Business model 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 model 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.