In the context of technical data integration, virtual earth GmbH places particular emphasis on modern information integration techniques such as ETL processes (Extract, Transform, Load) and a data-centric architecture. These approaches make it possible to extract, transform and efficiently store large volumes of data from various sources.
In today's fast-paced business world, managing knowledge is not just a matter of storing information, but a strategic necessity that determines the success of your business. Virtual earth GmbH is at the forefront of this development, offering holistic solutions that seamlessly integrate your organizational and technical knowledge management.
The simple subject-predicate-object data model of RDF tempts us to treat relationships and verbs as simple predicates or properties/properties.
However, this often does not do justice to reality.
For example, if we are collecting data on employment relationships or project contractors, a first approach might be:
A customer's question gave me the idea of writing down the influences on my modeling strategies and my own attitudes and guidelines.
These are my rules based on my current experience at the end of 2023. They are not universally valid but document the status of my development.
In the wonderful talk "Design in Practice", Rich Hickey talks about how important an up-to-date glossary is in the design process.
I have a similar view, and see something like "Business Knowledge Blueprints" as an important foundation for business applications and especially for data models and (enterprise) knowledge graphs.
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.
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.
A current customer project has once again shown me two important advantages of the W3C standards around semantic technologies and graph data:
Why RDF? Because it protects your investment!
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