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This post is related to my book, Semantic Webs of Meaning. Available on Technics Publications and Amazon. Use the code TP25 for a 25% discount if purchasing on the Technics Publications w…

In today’s rapidly evolving data analytics landscape turbo-charged by AI, businesses require an informationally-scalable infrastructure. “Enterprise Intelligence” offers a compreh…

This article accompanies my new book, Semantic Webs of Meaning: Building Contextual Knowledge Graphs for Deduction and Integration published by Technics Publications. This is an extension to the to…

An AI won't build a better ontology than you — but it can out-discipline you. Competency questions, SHACL validation, and a worked example, end to end.

Which AI Code Knowledge Graph Should Developers Choose in 2026?

Sovereign AI starts with semantics, not with infrastructure. Sovereignty is not only about where the model runs.

When meaning lives inside one application, every integration, acquisition, and replacement has to reconstruct it.

Data Engineering 1.0 moved data. Data Engineering 2.0 created Trusted Data Products. Data Engineering 3.0 creates the Context Layer that…

Stop choosing between structured knowledge and semantic search. Learn when to use Knowledge Graphs for explicit facts and Vector Databases…

Retrieval-Augmented Generation (RAG) improves large language models (LLMs) by retrieving relevant information from external sources and has been widely adopted for text-based tasks. For structured...

Graph Retrieval Augmented Generation (GraphRAG) has garnered increasing recognition for its potential to enhance large language models (LLMs) by structurally organizing domain-specific corpora and...

There is a lot of confusion around knowledge graphs and semantics. Much use and abuse of words, marketing claims, fluff and fake statements. Sometimes subtle, sometimes explicit. AI generated websites, AI generated articles, dashboards and stats. For some reason we went from 'data science' to 'AI' and completely dropped 'science'. Claims and analysis don't need evidence anymore. It ain't my style to shoot at anyone, but a widely-shared graph RAG production write-up does something rare: it contradicts itself in public, inside a single article 😣 In the opening we go from "accuracy jumped from 43% to 91%", halfway we read "...real production numbers 95% GraphRAG vs. 40–60% plain RAG..." and we end the report with "91% vs. 58%". Pick a baseline, any baseline, no dataset is named, no task or methodology, no metric. The piece also reports indexing cost at $7.13, and cost-per-document at $0.0048. Decimal-point precision on numbers that come from nowhere reproducible. It's precision theater, circus progress. Knowledge graphs and AI context architecture has a citation problem that hides behind good sounding details. Of course, none of this means that (graph) RAG doesn't work. If you do need solid references, have a look at the following: - When to use graphs in RAG: https://lnkd.in/eXXbVcec - GraphRAG bench: https://lnkd.in/e2sAs5f6 - RAG vs GraphRAG: https://lnkd.in/ep_VGt-v #GraphRAG #KnowledgeGraphs #KnowledgeAugmentedAI

Graph retrieval-augmented generation (GraphRAG) has emerged as a powerful paradigm for enhancing large language models (LLMs) with external knowledge. It leverages graphs to model the hierarchical...

Competency questions and invariants turn “the graph looks right” into something you can verify — shown on a 10-K filing graph anyone can…

Turning Millions of Documents Into an Agentic Knowledge Graph Why vector search breaks at scale — and how agentic graphs connect scattered facts so AI agents can actually reason across millions of …

We take a look back at the past season of Going Meta, this time with ontologies as the thread running through nearly every episode. This is…

|𝐁𝐅𝐎 "𝐯𝐬" 𝐃𝐎𝐋𝐂𝐄| Strange enough if we agree that those two are rivals, some of the strongest arguments are not really about classes, axioms or philosophical choices. They are much more practical: Who is actually using the ontology? Is it maintained? Is there documentation? A community around it? Are there organizations depending on it in real projects? And I think this opens a slightly uncomfortable question for ontology engineering: Can an ontology be technically excellent and still be, in practice, a failed ontology? Maybe at some point we should also judge them more like infrastructure. Not only “is this ontology good?” but also “is anyone actually able to build on it and keep building on it?” | 18 comments on LinkedIn

I regularly get the question whether using LLMs is acceptable for ontology creation. Usually it's not a question, but asking for confirmation or to determine whether I am 'one of those'. Here is the honest answer: I approve evidence-based decisions and pragmatic approaches. If it works for a particular use-case (including the budget, team expertise, timeline, technology), I will suggest a customer to go for it. I have no particular 'faith' in RDF, LPGs, LLMs, platform or company. I see business and innovation, not ideology. That said, there are interesting efforts to generate ontologies. The 'open-ontologies' project is an AI-native ontology engine, a Rust MCP server with tools for building, validating, querying, and reasoning over RDF/OWL ontologies. I love the "Single binary, no JVM" punch. The most interesting bit is not so much the project itself as the fact that from this you can see (in benchmarks) that: - open-world SHACL checks well-formedness, not completeness - zero focus nodes checks nothing, but it reports success - closed-world vocabulary checking is the missing primitive most knowledge augmented validation stacks don't have. Rephrased differently, open-world semantics can't catch fabrication. If your graph RAG pipeline validates LLM-extracted triples with plain SHACL and calls that governance, it isn't. #KnowledgeGraphs #SHACL #GraphRAG #KnowledgeAugmentedAI Open Ontologies: https://lnkd.in/eMrJvNFZ

This post was first published at metaphact’s blog in 2024 under the title How to approach semantic modeling: Perspectives from a metaphacts friend. I thought I would re-post it here for new readers, and also provide with some reflections at the end. Have my way of approaching semantic modeling changed in the past two years?