GraphNews

Introduction: Why Your Knowledge Graph Strategy Needs Multiple Ingestion Techniques

We establish connections between the Transformer architecture, originally introduced for natural language processing, and Graph Neural Networks (GNNs) for representation learning on graphs. We...

Knowledge Graphs shouldn't require a PhD

We’re building our startup on the hypothesis that semantic graphs are the best way to organize high-level world knowledge for AI. The central idea is to provide a system with a data store that can accommodate rich information about the world without losing its meaning, similar to how humans store and relate concepts in their minds. But if it is so convenient and promising, why is it not popular yet? We do not see a trend toward the widespread adoption of semantic systems in production. There are roughly two classes of graph data stores: graph databases (Neo4j, Amazon Neptune, etc.) and semantic stores (Virtuoso, GraphDB, etc.). When we talk about creating complex software systems, we usually think of the first class. They are simpler than the second class and therefore more reliable. The problem is that these stores are not semantic. They are very simplified graphs that allow only slightly more convenient data retrieval. The second class is better suited to semantic graphs. However, they are very complex, unintuitive, used in narrow specialized cases, and difficult to implement effectively in agentic systems. It turns out that: Graph databases are not truly semantic. Semantic data stores are not truly databases. Here is the point: The prospects for using semantic graphs to develop AI systems are unclear, as graph databases are generally considered the first working prototypes of this idea, yet in reality they offer little advantage. In turn, true semantic stores are complex and entail numerous costs, which is why they have not gained popularity. Implementing semantic graphs should be as easy as setting up a NoSQL database. Then this technology will have a significant impact on the development of agentic systems. That is why we consider the development of such technologies important, and this is what we are actively researching and applying at Milkyweb. | 33 comments on LinkedIn

An Overview on Graph Types for Graph RAG: Structures, Schemas, and When to Use Each One

Ontology Design Patterns Part 6: *Multiple Inheritance, Not Multiple Instantiation*

#Anthropic just disrupted the first AI-orchestrated cyberattack.

Earlier this year, I discovered Larry Swanson podcast on Knowledge Graphs. One of the first episodes I listened to was with George Anadiotis Six months later, I finally met George in person. And not just George! Amy Hodler from GraphGeeks was there too, hosting an excellent event on graph technologies. I’m still quite new to the graph tech world. My initial interest came from exploring how graphs can support legal reasoning and inference in LLMs, mainly because graphs help introduce logic, determinism, and reduce hallucinations. But this event (and the people there) helped me understand how much broader the applications really are. One of the first things I learned was that graph technologies largely fall into two families: Label-Property Graphs (LPG) and RDF. With the help of the experts onsite, I explored the most common use cases for each. For LPG (structured knowledge): • Pattern recognition – Finance: fraud detection, anti-fraud behavior chains (something SQL can’t trace) – Compliance & risk: the London Stock Exchange uses LPG to trace paths leading to risk concentration for DORA compliance • Route / path finding – Cybersecurity: mapping exploit paths – Supply chain: modelling supply routes and comparing alternatives – Incident analysis: understanding causal chains inside complex systems For RDF (built for meaning and semantics): • Domain modeling and knowledge engineering • AI memory architectures There were also discussions about hybrid approaches where both frameworks work together: Natural language query → grounded semantically with RDF → executed through an LPG engine. In practice, this looks like: LLMs providing the interface, RDF providing the semantics, and LPG providing the performance. A powerful combination for building the next generation of intelligent systems. Thank you George, Amy, Maja and everyone else for the insights and conversations. And thanks to GraphGeeks and Connected Data for bringing such a strong community together.

2026 Is the Year Ontology Breaks Out — And Why Getting It Wrong Is Dangerous Neil Gentleman-Hobbs is spot on. 2026 is the tipping point where enterprises finally realize that AI can’t act intelligently without a semantic foundation. LLMs can produce language, but without ontology they cannot understand anything they output. A while back I wrote the Palantir piece about Alex Karp’s “chips and ontology” quote — and the reaction was huge. Not because Palantir isn’t doing important work, but because there’s a major misunderstanding in the market about what ontology actually is. And here’s the core truth: **Palantir is not doing ontology. They are doing a configurable data model with workflow logic.** Their “ontology” has: • no OWL or CLIF • no TBox or ABox structure • no identity conditions • no logical constraints • no reasoner • no formal semantics at all It’s powerful, yes. It’s useful, yes. But it is not ontology. Why this is dangerous When companies believe they have ontology but instead have a dressed-up data model, three risks emerge: 1. False confidence You think your AI can reason. It can’t. It can only follow whatever imperative code a developer wrote. 2. No logical guarantees Without identity, constraints, and semantics, the system cannot detect contradictions, errors, or impossible states. It hallucinates in a different way — structurally. 3. Brittleness at scale Every new policy or relationship must be coded by hand. That’s not semantic automation, it’s enterprise-scale brute force. This is exactly where systems crack under combinatorial growth. In other words: you don’t get an enterprise brain, you get a beautiful spreadsheet with an API. At Interstellar Semantics, this is the gap we focus on closing: building real ontologies with identity, constraints, time, roles, and reasoning — the kind of foundations AI systems actually depend on. If your organization wants to understand what real ontology looks like: 👉 https://lnkd.in/ek3sssCY #Ontology #SemanticAI #EnterpriseAI #AI2026 #Palantir #SemanticReasoning #KnowledgeGraphs #DataStrategy #AITrustworthiness | 12 comments on LinkedIn

Accounting for How We Work

𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗪𝗶𝘁𝗵 𝗚𝗿𝗮𝗽𝗵𝗥𝗔𝗚: 𝗗𝗼𝗻’𝘁 𝗕𝘂𝘆 𝗮 𝗠𝗮𝗽 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗔𝘀𝗸𝗶𝗻𝗴 𝗪𝗵𝗼 𝗨𝗽𝗱𝗮𝘁𝗲𝘀 𝗧𝗵𝗲 𝗧𝗲𝗿𝗿𝗮𝗶𝗻

Nuix Ltd (ASX: NXL) has agreed to acquire French graph-intelligence company Linkurious SAS, strengthening the company’s data analytics and visualisation...

Continuing our exploration of Knowledge Graphs and LLMs: a systematic analysis of RAG techniques for Intelligent Advisory Systems

Huge news for Cosmograph 🪐 While everyone was on Thanksgiving break, I was polishing up the next big Cosmograph update, which I'm finally ready to share! More than three years after the initial release, Cosmograph remains the only single-node web-based tool capable of visualizing graphs with 1 million points and way more than a million links due to its unique GPU Force Layout and Rendering engine cosmos.gl. However, it also had a few major weaknesses like poor memory management and limited analytical capabilities. Version 2.0 of Cosmograph solves these problems by incorporating: - DuckDB (the best in-memory analytics database); - Mosaic (the fastest cross-filtering and visual analytics framework for the web); - SQLRooms (an open-source React toolkit for human and agent collaborative analytics apps by Ilya Boyandin) as its foundation; - The latest version of cosmos.gl (our core force simulation and rendering engine that recently joined OpenJS) to give you even faster performance, more forces, and the long-awaited point-dragging functionality! What does this mean in practice? - Work with larger datasets and use SQL (thanks to WebAssembly and DuckDB); - Much better performance (filtering, timeline, changing visual properties of the graph, etc.); - Open Parquet files natively; - Save your graphs to the cloud and share them with the world easily. And if you work with ML embeddings and love Apple's Embedding Atlas (https://lnkd.in/gsWt6CNT), you'll love Cosmograph too since they have a lot in common. If all the above has excited you, go check out Cosmograph's new beautiful website, and share the news with the world 🙏 https://cosmograph.app | 41 comments on LinkedIn

Summary of the things I learned at the 2025 International Semantic Web Conference, my scientific home.

What is knowledge engineering?

After seeing one too many Neo4j tutorials teaching developers to hide nouns inside relationships, I’ve had enough. Here’s why this…

The global graph database market size is projected to grow from $2.85 billion in 2025 to $15.32 billion by 2032, exhibiting a CAGR of 27.1%

Summary of what I learned at Connected Data London (CDL2025). The winners are clear: companies who have quietly been investing in knowledge graphs for the last decade, way before this current AI wave

Here it is - the draft of the latest chapter on meaning in data modeling.

Ontologists, rejoice!

LPG is NOT a hypergraph

Explore ~ 6,000 neurips papers in hundreds of clusters with Cohere Command A and Embed-v4 models

I just stepped off the stage at Connected Data London, where we talked about the black box of AI and the critical role of ontologies. We ended that talk by saying that we, as a community, have a responsibility to cut through the noise. To offer clarity. That is why today, we're launching The Knowledge Graph Academy. For too long, education in semantic technology has tended to sit at one of two extremes: highly abstract academic theory, or tool-focused training that fails to teach the underlying principles. We are building something different. Where educational rigour meets real-world practice. And I’m not doing this alone. If we are going to define the field, we need the leaders who are actually out there building it. I am incredibly proud to announce that I’ve teamed up with two of the sharpest minds in the industry to lead this programme with me: 🔵 Katariina Kari (Lead Ontologist): Katariina has spent years building KG teams at retail giants. She knows exactly how to capture business expertise to drive ROI. She’s the master of the philosophy: "a little semantics goes a long way." 🔵 Jessica Talisman Tallisman (Senior KG Consultant): With 25+ years in data architecture, Jessica is a true veteran of the trenches. She’s a LinkedIn Top Voice, an expert on the W3C SKOS standard, and creator of the 'Ontology Pipeline' framework. This isn't just training. It’s a shift in mindset. This course doesn't just teach you which syntax to use or buttons to press, The Knowledge Graph Academy is designed to change how you think. Whether you are a practitioner, a leader shaping AI strategy, or someone looking to pivot your career: this is your invitation. Let’s turn ideas into understanding, and understanding into impact. ⭕ The Knowledge Graph Academy: https://lnkd.in/ecQBMCg3 | 59 comments on LinkedIn

The company scraped 150,000 posts and ran community detection algorithms to identify which Stranger Things fan groups have the best track records for predictions. Theories were mapped as a graph (234k nodes and 1.5M relationships) that track characters, plot points and speculation and then used natural language processing to surface patterns across seasons. These predictions are then mapped out in a visualization for extra analysis. Top theories include ■■■ ■■■■■ ■■■■, ■■■ ■■■■■■■■ ■■ and ■■■■ ■■■■■■■■ ■■■ ■■ ■■■■. (Editor note: these theories have been redacted to avoid any angry emails about spoilers.)

OSMnx is a Python package that downloads any city’s street network, buildings, bike lanes, rail, or walkable paths from OpenStreetMap and instantly turns them into clean, routable NetworkX graphs with correct topology, projected coordinates, edge lengths, bearings, and travel speeds.

The Sketch and The Blueprint

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