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A knowledge graph is structurally lying to you over time. Every triple you extracted last year sits in your graph with the same epistemic weight as the triple you extracted this morning. The graph has no concept of belief decay. It cannot distinguish between a fact and a fossil. Temporality in knowledge graphs is almost always conflated with timestamps. A created field on a node is however not temporal modeling. It records when the assertion entered the system. It says nothing about the interval during which the assertion is valid, nor about the rate at which your confidence in it should erode. Bitemporal modeling has existed for a long time, yet almost universally absent from production. The deeper problem is epistemic decay. Expiration assumes you know when a fact stops being true. You rarely do. What you actually have is a credence (a degree of belief) that decreases as a function of time, domain volatility, and source reliability. I'll refrain talking about Bayesian networks but do look it up. All of this is really a prior that should propagate through your graph via confidence-weighted traversals. The consequence for GraphRAG is a retrieval safety problem. Stale nodes with high historical centrality accumulate epistemic authority they no longer deserve. When your retrieval layer surfaces context for an LLM, it preferentially returns high-centrality nodes. If those nodes encode the organizational reality of three years ago, your agent reasons with great confidence from a false prior. This is historical hallucination in the graph in a way, laundered through retrieval, and then likely blamed on the LLM. What temporal degradation requires in practice is an edge-level decay functions, parameterized per ontology class, not global, not uniform. Confidence propagation on traversal, not just on storage. Re-extraction triggers when node confidence drops below a domain-specific threshold. And more importantaly a query API that exposes temporal confidence as a first-class return value, not a hidden metadata field that analysts never see. The graph should be able to tell you: I believe this, but I believed it more strongly two years ago. Until it can, you don't have a knowledge graph (or agent memory or context graph). You have an append-only log with a SPARQL/GQL endpoint. #KnowledgeGraphs #Semantics #RDF #ContextGraphs

LLM-based agents for industrial asset operations show limited accuracy when reasoning over flat document stores. AssetOpsBench (KDD 2026) establishes that GPT-4 agents achieve 65% on 139...

Discover why enterprises are moving beyond vector databases for AI, the limitations of vector search for complex reasoning, and how ontology-grounded retrieval enables more accurate, auditable, and reliable results for multi-hop queries and compliance tasks.

Everyone is suddenly racing to sell you your own context. But a context you can buy is a context your competitors can buy too. Barely a week passes now without another "context layer", "context graph", or "context foundation" for your AI agents - your organisation's reasoning, its exceptions, its hard-won workarounds, packaged up and ready to plug in. The whole industry is starting to agree that context is the missing piece. It sounds like precisely what the moment demands. But here is the kicker - it is precisely the thing you cannot just buy. Here is why. Your context is not a feature you bolt on. It is your model of your own world - the thing that lets your organisation perceive, predict, and act as a single, coherent entity. Every system that survives does the same thing: it holds a boundary between itself and the world, and works without pause to keep what's inside coherent against a world that never stops trying to throw it off. That boundary is the difference between being a system and being a pile of parts. Your connected data, your ontology, your formalised meaning is that boundary. So look hard at what the "context foundation" you purchase actually is. This is subtle, and it relates to the point that you can outsource intelligence, but you can't outsource understanding. There's a very real sense in which an organisation's own agency can be quietly sequestered by helpful platforms that offer to make your life so much easier. If you outsource too much of the process of connecting your context, you have outsourced your understanding of yourself - and handed the boundary that defines you to a third party. This is the line the agent era forces every organisation to draw. You can buy the models. You can buy the agents. You have to build the context. You cannot buy the boundary - because the moment your context is defined outside your control, you are no longer a distinct system. You are a component inside someone else's. And here is what trips people up: building the boundary yourself does not mean walling it off. A boundary made of proprietary, vendor-specific formats is just a smaller cage - you have swapped one supplier's cage for another's. It only stays yours if it is woven from open standards: shared identifiers, shared semantics, ontologies no single vendor owns. Openness is not the opposite of ownership. It is the only durable form of it. ⚡ A context you can buy is a context your competitors can buy too ⚡ The meaning is yours - owned, connected, and maintained from the inside. That is not a product you procure. It is the hard work of becoming one connected system. ⭕ You cannot outsource understanding: https://lnkd.in/eegNhfUG ⭕ System Boundaries: https://lnkd.in/dE8aVW_V 🔗 The Knowledge Graph Guys: https://lnkd.in/ezHU2amU | 11 comments on LinkedIn

Many companies come to us asking for advice about private context. They all start the same way: "how do we make our context usable for agents?" The phrasing is always the same.

A field guide to what it is, what it is not, and where it fits in your AI architecture. I have had some version of the same conversation with a CIO almost every day this year.

Agentic AI is exposing a foundational gap in most enterprise data strategies: Data without meaning is unusable for autonomous systems. Agents don’t just retrieve data — they interpret, decide, and act. Without explicit context, they guess. And when agents guess, they get joins wrong, misinterpret metrics, and act on flawed assumptions. This is why ontologies, […]

Start with a fixed, generic base and extend only when your data demands it.

Cyber threat intelligence (CTI) reports now serve as essential resources for capturing adversary tactics, techniques, and procedures observed in modern attack campaigns. While traditional CTI...

This is part 2 of a three-part series on the evolution from graphs to knowledge graphs to context graphs and why connected data is becoming foundational for enterprise AI. In the first post, I outlined the difference between graphs, knowledge… Read more →

This is part 1 of a three-part series on the evolution from graphs to knowledge graphs to context graphs and why connected data is becoming foundational for enterprise AI. In this series, I’ll look at what these terms mean, why… Read more →

Your agents stall because they have no model of your business. That model has a name -ontology — and AI just made it buildable.

Learn the 6 causes of ontology decay and how Data Developer Platforms help maintain semantic accuracy, governance, and AI readiness.

From Data Hygiene to Contextual Logic

Learn how operationalising AI ontologies improves enterprise AI with semantic context, governance, explainability, and aligned business reasoning across AI systems and agents.

What That Means for the Data That Crosses Your Boundaries.

Dr John Beverley’s STIDS presentation shows why AI needs more than connected data: it needs engineered meaning, systematic disambiguation, and semantic infrastructure that can survive real enterprise

The accurate claim isn’t that OWL is outdated, it’s that it got demoted. It was sold as the reasoning layer of the Semantic Web — the future of all data — and that vision died. What’s left is a specialist tool, and using it outside its narrow zone is now usually the wrong call. Three things hollowed it out: The market chose property graphs. Neo4j/Cypher and now GQL as an ISO standard own industrial graph work. Developers never adopted RDF/OWL’s ergonomics — the description-logic mental model, RDF/XML, Protégé, all of it reads as academic, and for good reason. For most graph use cases the open-world, no-unique-names semantics is a liability you have to fight, not a feature. SHACL ate the useful half. The work people actually needed — closed-world validation, “does this data conform” — is what SHACL does, and that’s most enterprise demand. OWL’s inference got left as the part nobody invokes. And even where people do want inference, the momentum has moved toward Datalog-style rule materialization (RDFox and friends) over DL tableau reasoning, because it’s faster and more predictable. So “OWL reasoning” is being supplanted from inside the symbolic camp too. The OWA mismatch. Almost all enterprise data work is closed-world. OWL’s open-world assumption is the opposite of the engineer’s instinct and is the single most common source of “why doesn’t this behave like a schema” pain. The exception: where formal classification and consistency checking are genuinely load-bearing, nothing replaces OWL, and it’s not a toy there. SNOMED CT, Gene Ontology — massive, in production, running real healthcare — are OWL EL, which reasons in polynomial time over hundreds of thousands of classes. The “doesn’t scale” critique is about OWL DL full; the profiles people actually deploy scale fine. So OWL is alive and irreplaceable in domains that need automated subsumption and consistency. If the “symbolic backbone” is doing real classification and consistency inference, OWL EL is defensible. If it’s really doing validation and constraint enforcement, that’s SHACL plus rules over a property graph, and reaching for OWL there is exactly the dated, academic move the skeptics are pointing at. The thing that’s outdated is OWL as a default. OWL as a scalpel for inference-heavy domains is fine.

The Principles of Organizing as First Principles for AI Every organization building toward AI increasingly wants a knowledge graph, a semantic layer, a governed catalog and something that lets machines reason over the company’s resources. Most of them start by buying that solution. The platform arrives, the connectors get wired, and then the question invariably emerges, organized according to what?