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Knowledge Graphs

Ein Knowledge Graph ist eine innovative Methode zur Strukturierung und Organisation von Informationen. Er ermöglicht es, Daten und deren Zusammenhänge in einem Graphenmodell zu erfassen, was effektives Navigieren und Verstehen komplexer Informationssysteme ermöglicht.

Unsere Expertise

Unser Team verfügt über umfangreiche Erfahrung in der Entwicklung und Implementierung von Knowledge Graph-Lösungen. Wir unterstützen Sie bei der Strukturierung Ihrer Informationen, der Modellierung von Zusammenhängen und der Entwicklung von Anwendungen, die auf Ihrem Knowledge Graph aufbauen.

Vorteile eines Knowledge Graphs

  • Besseres Verständnis komplexer Informationen und Zusammenhänge
  • Effiziente Navigation und Exploration von Daten
  • Möglichkeiten zur Automatisierung von Prozessen und Wissensmanagement

Erfahren Sie mehr über unsere Knowledge Graph-Dienstleistungen und wie wir Ihnen helfen können, Ihre Daten optimal zu nutzen. ```

By mathiasp, 21 March, 2023

Investment protection: an important advantage of RDF and SPARQL

A recent client project has again brought to my attention two important benefits of W3C standards around semantic technologies and graph data:

  • Stability, in the sense of portability of data and queries
  • Choice of products between which I can exchange my data and queries
  • And thus investment protection that proprietary graph databases do not offer

tl;dr

Why RDF? Because it protects your investment!

:Brutus :stabbed :Caesar → But when exactly?

Tags

  • event semantics

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.

By mathiasp, 8 May, 2023

Explaining OWL: use 'set' for 'class', it's more precise and less likely to be confused with OO classes

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'.

By mathiasp, 19 April, 2023

Starting ontology creation from existing taxonomies

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 :)

Tags

  • Knowledge Graphs
By mathiasp, 6 April, 2023

GPT: statistical AI (LLM) vs symbolic AI (Semantic Web)

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.

By mathiasp, 4 February, 2023

Business Model Blueprints

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.
 

Topic
Knowledge Graphs
Modellieren

Logic, Semantic and Data Modeling

Logic and semantics play a critical role in data modeling, as they help to structure and organize data in a clear and meaningful way. By using logical and semantically rich data models, businesses and organizations can better understand and utilize their data to make informed decisions and drive growth.
Knowledge Graphs

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