bergis universe of software, hardware and ideas

The origin of most data in the world comes from closed-world systems. Forms, logging systems, and sensors all generate data in predefined structures where the schema is known in advance. Yet when people enter the world of RDF, interested only in adding semantics to their data, they’re confronted with an all-or-nothing proposition: “Here’s a triple store, learn OWL, RDF/XML is bad but your code must be able to process it, accept that you live in an open-world. Take it or leave it.”

But many use cases don’t require the full semantic web stack. This post explores how closed-world systems can benefit from RDF semantics without adopting the full semantic web stack. We’ll cover when closed-world assumptions make sense, how to bridge closed and open-world systems, practical examples from sensors, UIs, and IoT, new SHACL features in development that enable these patterns, and what the RDF community needs to do to make this easier.

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OpenAI has recently introduced the Agentic Commerce Protocol (ACP), which enables shopping and purchasing directly within ChatGPT. That is a step forward for agentic applications. Still, it only scratches the surface of a much broader problem: How to safely share sensitive or contextual data between services in large language model (LLM)-driven applications without exposing it to the LLM.

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The experimental branch of shacl-engine is now introducing features related to the upcoming SHACL 1.2 specifications, along with some proposals still under discussion. These additions are in an early stage of development, meaning they may be incomplete or unstable. But you can start playing at the experimental instance of SHACL Playground.

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Many blog articles and LinkedIn posts claim that Knowledge Graphs and Language Models are a good match and that potential future products that combine both worlds will be even more powerful. I work in that field because I strongly believe in that idea, but it requires more than a blog post with theoretical claims to convince people. That’s why I wrote this blog post with easy-to-understand examples that will work in the upcoming 8.2 release of TopBraid EDG and only require a basic understanding of SPARQL, JavaScript, and SHACL. If I haven’t sparked your interest yet, let me tell you that everything will be explained based on examples with wombats!

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Knowledge Graphs combined with modern language models is still a hot topic. We have seen many demos in the past, and increasingly, they have become features in products, but most of the time, they are connected inefficiently. In this blog post, I will take a closer look at the essential building blocks of both worlds, their features, what makes them unique, and how they could be combined on a native level.

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I hoped the “Leaving the Sinking Zazuko Ship” blog post would be the last post about Zazuko, but then they stole my code.

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While working on RDF/JS specifications, I fulfilled two roles.

As the chair, I had the goal that every feasible requirement submitted could be implemented based on what is written in the specification.

Being the author and maintainer of RDF-Ext, I contribute requirements to the group as well. In this blog post, I want to provide you with a closer look at one core idea I pushed forward - an idea that, until now, was spread over multiple discussions and comments on different GitHub issues.

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After almost 9 years, I left Zazuko, the company I co-founded, for good at the end of June. Like I share my open-source libraries with you, I was hoping you could benefit from the lesson I learned about how a dark triad personality ruined a company.

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Graph traversing with plain RDF/JS objects is no fun. Keeping track of the path walked while traversing a graph adds another layer of complexity. With Grapoi, graph traversing is fun again and makes code more readable.

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I guess it was not only my social media stream that was flooded with GPT4 news in the last few days. The results are impressive but not surprising to me. A combination of events, which are more important from my point of view, didn’t get that much attention: The LLM LLaMA runs on a Raspberry Pi, and Stanford students fine-tuned the model for $100.

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