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Let me start with a definition that differs from popular ones (e.g.

Lesson 7: You Need a Methodology

If you lead data or technology for a large enterprise, you have probably lived through the same sequence of data migrations. Data warehouses gave way to

A Review of Standard Ontologies That Exist for Decades

When building graphs, it’s tempting to reuse the same relationship type names across different parts of the model. But in both Neo4j and…

📊 Context Graph Series 🧠 Beyond Knowledge Graphs and Hypergraphs : Context as a Control Plan (Graphs decide how you store. Context Graphs decide how you act.) There's a lot of discussion around graphs — knowledge graphs, hypergraphs, higher-order networks, world models. All important. But they're still answering the same question: 👉 How do we store complex information? Context Graphs answer a different question: 👉 How do we decide what can be acted on — here, now, and by whom? 🧱 The Storage Problem is Solved Knowledge Graphs gave us triples. Hypergraphs gave us hyperedges. Property Graphs gave us rich attributes. Storage keeps getting better. But enterprise AI systems still fail. Not because data is missing. Because decisions are reused without standing. 🏢 Real Enterprise Example: Atlas Systems Atlas has a knowledge graph. Entities. Relationships. Policies. It even has a hypergraph for multi-party vendor agreements. Storage was never the problem. An agent retrieves a valid SLA exception. ✔️ Correctly stored ✔️ Correctly retrieved ❌ Applied in wrong region ❌ After its validity window ❌ Without original authority $280K overrun. Compliance violation. The graph was correct. The decision was not. 🧠 What a Context Graph Actually Does A Context Graph sits above storage. It doesn't replace: 🔹 Knowledge Graphs 🔹 Hypergraphs 🔹 Property Graphs It works on top of all of them. Before any action, it asks: ✓ Who approved this? ✓ Under what authority? ✓ For which scope? ✓ During what time window? ✓ With what evidence? If standing matches → Execute If not → Block or Escalate That's governance at runtime — not after the fact. 🤖 Why This Matters for AI Agents AI agents don't invent most enterprise failures. They scale existing ones. Without a Context Graph, agents retrieve correctly, reason fluently, act confidently — and still fail audits. Because correctness is not permission. ⚠️ Wrong region? Not a data error. Past time window? Not a hallucination. No authority? Not a retrieval failure. It's a governance failure — no graph structure alone can prevent it. 🔍 Why This Conversation Is Accelerating Ashu Garg and Jaya Gupta helped name this shift — Context Graphs. Arvind Jain and Dharmesh Shah stress durable decision traces across systems. Kirk Marple calls it a clear signal of where enterprise AI is heading. The pattern is clear: storage is solved. Governance is not. 💡 Architect Takeaway The last generation of systems optimized for answers. The next generation must optimize for judgment. 🔹 Graphs decide how you store. 🔹 Context Graphs decide how you act. That's why this layer can't be bypassed. #ContextGraphs #EnterpriseAI #AgenticAI #AIArchitecture #KnowledgeGraphs #Hypergraphs #Governance Kurt Cagle Anthony Alcaraz Jessica Talisman Raphaël MANSUY | 10 comments on LinkedIn

In this annual segment, EK CEO Zach Wahl defines what he considers to be the top trends in Knowledge Management for 2026.

How I Turned 144 Pages of Regulation Into One Clickable Map.

Most of what we call a “knowledge graph” is still too close to the semantic layer mindset: great for measurement, weak on meaning.

The enterprise AI world has a new obsession: the Context Graph. Venture capital is flowing into it, and engineering teams are racing to build it.

A Practical Perspective Grounded in Labeled Property Graphs and Applied Knowledge Graphs

Table of Contents

[I’m thrilled to announce that the source code for neo4j-gspatial is finally open-source!

Why OWL Profiles Exist and Why Production Keeps Ignoring Them

Most business intelligence is tacit. It lives in people's heads, emails, and chats rather than in transactional databases. In theory, Context Graphs capture an audit trail of these informal interactions so that AI doesn't just follow rigid rules, but understands the intent and exceptions that make a business actually run. In that sense, Andreas Blumauer of Graphwise points out that a context graph is a kind of knowledge graph. “The consensus across the community ,” he says, “ is that a Context Graph is an operationalized Knowledge Graph.”

How science, regulation, collaboration, and public funding shaped the world’s most mature semantic infrastructure

Semantics, standards, and structure: how SKOS, taxonomies, and controlled vocabularies power interoperability, governance, and meaning at scale in modern data ecosystems

Learn what graph technology experts and practitioners believe the year ahead has in store for the world of connected data – from AI to hypergraphs and more.

Plagiarism, VC Marketing, and the Erosion of Truth There’s a particular kind of intellectual dishonesty that thrives in the technology industry. It doesn’t look like plagiarism in the traditional sense—no copied paragraphs, no lifted sentences.

Part 1b of The Ontology Imperative: Building Trustworthy Agentic AI

Why RDF stores don’t overwrite values on import — and what to do instead

This report presents findings from APQC’s 2026 Knowledge Management Priorities and Trends Survey, with insights from global participants across industries and roles. The research explores how KM teams are adapting to rapid technological change, especially the integration of AI, and identifies the top priorities, opportunities, threats, and skillsets shaping the future of KM.Key highlights include:

Following the acquisition of Vodafone Italy by Swisscom, these European telco leaders saw an opportunity to rethink how they serve customers.

Graph Databases for RAG + How to choose

From Structured Queries to Natural Language

Preamble The rapid and pervasive expansion of generative artificial intelligence (GenAI) technologies has highlighted the inherent limitations of purely neural approaches, as well as the benefits o…

We've released `kglab` version 1.x, which is long overdue -- this should have been positioned as a full release years ago!