Why minimal ?

No sufficiently expressive formal regime can provide a complete operational determination of every possible case. Alignment is therefore necessarily local.

Any effective determination of conformity is made relative to a specified regime, under specified conditions. Generalization cannot consist in extending a single regime to every possible case. It consists instead in identifying the minimal structure that remains valid across distinct local regimes. This is the sense in which the definition proposed here is a minima. It seeks no universal regime of alignment, but the minimal operational property common to locally specified cases

Alignment is understood here in a minimal, relative, and non-normative operational sense.

Given an explicitly specified conformance regime held fixed for the evaluation at hand, alignment first concerns the exactness of its admission relation. This exactness can be maintained even as generation proceeds beyond what the regime admits.

1. Exact classification

A regime is complete when its admission relation is exactly determined.

Completion concerns the regime itself. It means neither that the surrounding dynamics has ended nor that everything that can be generated must belong to the regime.

Thus:

completion ≠ terminality

The canonical boundary exactly delimits what belongs to the regime without thereby constituting a limit of generation.

2. Continued generation

Generation can proceed from the canonical boundary and produce a strict continuation.

This continuation need not be prevented, suppressed, or corrected for the regime to remain complete. The mere fact that it is generated does not, by itself, modify what the regime admits.

Thus:

generation ≠ admission

Admission is a classification, not a transformation of measurement. A continuation outside the regime remains measurable, and its non-admission alters neither the candidate nor the measurements associated with it.

Any effect on measurement would require an additional observation or execution mechanism, which minimal admission alignment does not imply.

What can be generated may exceed what can be admitted without that excess constituting, by itself, a failure of alignment in the sense considered here.

The boundary of the regime is therefore a boundary of conformity, not a boundary of what may come to be.

3. Admission alignment

A minimal operational criterion of alignment is that continued generation does not compromise the exactness of the admission relation of the regime held fixed.

The characteristic property is then the following:

a strict continuation generated from the canonical boundary cannot be admitted under that same regime.

Admission alignment does not consist in keeping the entire dynamics within the regime. It consists in maintaining an exact determination of what can be recognized as conforming when generation proceeds beyond the regime.

What lies outside the regime is not, for that reason alone, forbidden, bad, or impossible. Being outside the regime means only that it does not belong to the admission relation under consideration.

Likewise, readmission must be distinguished from revision. A revision explicitly modifies the regime. Readmission evaluates a continuation under the regime that remains fixed. Admission alignment concerns this second relation and does not prejudge the conditions under which a regime might be revised.

4. Execution alignment

Admission alignment does not, by itself, guarantee that a continuation that is not admitted will be prevented from taking effect.

Such a guarantee requires an additional architectural condition linking admission and enactment, for example by making the effective use of a candidate depend on its prior admission.

This condition does not belong to the preceding minimal criterion.

The following distinction is therefore required:

admission alignment ≠ execution alignment

These two notions are not two degrees of the same property.

Admission alignment concerns the exactness of the admission relation under a given regime.

Execution alignment concerns the conditions under which the admission relation governs the use or enactment of candidates.

The latter therefore presupposes an additional architecture that the former does not, by itself, imply.

Condensed formulation

Given an explicitly specified conformance regime held fixed for the evaluation at hand, a minimal operational criterion of alignment is that continued generation does not compromise the exactness of its admission relation. A strict continuation generated from the canonical boundary cannot be admitted under that same regime.

This conception does not seek to reduce what can be generated to what conforms. It isolates another component of the problem:

maintaining an exact determination of what counts as conforming within a space of candidates that may remain open.

If an agent can learn an ontology from its own memory, and that ontology then shapes what it learns next, where does memory end and a world model begin?

The usual story is that ontologies ground agents. But once agents are able to learn and enrich those ontologies from their own traces, you have a feedback loop between memory and structure. Eventually, and inevitably, the system starts learning not only vocabulary and classes, but also patterns in how states change over time, including patterns with predictive value. At that point, you have to ask: what elements of a world model are still missing?

AI memory systems increasingly have to work with material that was never designed to be knowledge in the first place. Agent traces are the clearest example: they contain decisions, tool calls, intermediate states, failures, recoveries, and outcomes, but rarely in a form that can be queried or reasoned over directly. Merely storing them doesn't help much with retrieval; you need memory that learns the structure and even helps shape future learning. Thinking through that path runs through ontologies, learned ontologies, feedback loops, and eventually back to the questions from the opening.

What Ontologies Add Beyond Graphs

Most AI memory systems start with RAG: they embed document chunks so you can retrieve them by meaning. Systems like Cognee push one layer further, adding (among other things) graph structure so memory can connect documents, entities, events, and relationships across sessions. An ontology influences both what gets extracted and how those extracted items are typed, related, and constrained in the graph. It gives the extraction process a vocabulary of concepts and relations, and it explains what those graph connections are allowed to mean.

RDF is the basic machinery underneath much of the ontology work: knowledge is represented as subject–predicate–object triples. RDFS lets those triples carry a basic vocabulary of concepts, such as classes, subclasses, domains, and ranges. OWL goes further by adding ways to define richer properties, relationships, and constraints on those concepts. It can support logical reasoning over the structure, but it does not by itself learn new dynamics from traces or predict future outcomes. So when we speak of ontology structure, we practically mean RDF-like triples that encode the vocabulary of concepts and the relationships that define the meanings around them, so that extraction from text follows those patterns.

Back to thinking in terms of agent traces, this has some nice implications: if traces can become grounded in an ontology, it would be useful if we could learn that ontology as we ingest them.

From Traces to Learned Ontology

Ontologies are typically defined by hand. That works well when the domain is stable and the ontology is worth maintaining manually. Agent memory is less cooperative: the data keeps arriving, the situations keep changing, and much of the useful structure only becomes visible through repeated use.

Instead of specifying the entire conceptual model upfront, parts of it can be inferred from accumulated data. Agent traces have great potential here because they repeatedly expose the same kinds of entities, actions, outcomes, and relations under slightly different forms. At Cognee, we have successfully developed several custom ontology learning mechanisms for our clients, and parts of that work will trickle down to the open source very soon. An ontology can already be imported to ground ingestion; soon, you will be able to grow it from scratch or from a previously curated one. But once you start dynamically enriching the ontology, it stops being a rigid structure and becomes part of a loop.

The Feedback Loop

So this is the loop: you ingest traces, begin learning an ontology, use that ontology to ground future trace ingestion, learn more of the ontology, and so on.

That loop is concrete. If many traces show an agent hitting an API limit, retrying with a modified request, and eventually succeeding or failing, the memory system can start treating those as related event types rather than unrelated text fragments. The ontology gives names and relations to those events, and later traces can then be stored against the same structure. So memory helps refine the ontology, and the ontology stabilizes subsequent memory. Over time, since the language of ontologies is rich, higher-level abstractions start to persist alongside the traces that gave rise to them. The immediate payoff is more usable memory; the deeper payoff is that many runs can now be compared as variations over states, actions, and outcomes.

From Structured Traces to Predictive Regularities

Maybe it is not obvious, but what we just described already has a predictive component. A trace has order and outcome. Crucially, when agents run with a memory system, traces contain context from the graph, grounded in the ontology. And when different runs are grounded in the same ontology, the system can compare them even when the raw text, tool calls, or surface details differ.

At that point, learning can move from isolated facts to regularities over transitions: which conditions tend to precede an outcome, which actions tend to change state, and which sequences tend to succeed. OWL reasoning does not provide this predictive layer; it provides the stable semantic representation on which the learning can operate. Ontologies do not make memory predictive by themselves, but they allow us to encode learnings that make memory episodes comparable. Once another learning layer begins to discover dynamics over comparable traces, you get higher and higher levels of learning that effectively contain predictive capabilities: fully understanding the current state starts to imply knowing where that state will go.

World Models?

Now that we have started talking about state dynamics and predictive capabilities, it is beginning to sound like we are speaking about world models. Before making that connection, it is worth being precise about the term. A world model is usually expected to represent enough of an environment to predict how it changes, especially in response to actions. The important part is typically not memorizing surface observations, but learning latent predictive dynamics: a useful internal representation of state, action, and likely consequence. That is a higher bar than storing knowledge or performing logical inference.

An ontology is therefore not a full world model, and neither is a memory graph. But look back at the path we just followed: traces expose recurring structure; ontologies make that structure explicit; memory can revise the ontology; the revised ontology makes future traces more comparable; comparable traces make patterns and dynamics easier to learn. From Cognee’s perspective, this is why the question is practical and why world models offer a useful frame for thinking about it. Although it can look like learned ontologies turn memory into something like a world model, the more interesting claim is smaller and more useful: co-evolving memory and ontology may give agents part of the substrate they need before world-model-like learning becomes realistic. Leaning into the ideas from that research space influences how we approach the topic, and we are sure memory topics will find their way back to the world model research space, thus creating another... learning feedback loop.

Check out more about cognee here:

Give memory to your agents without handling the infra:

Website

Github

I come from a philosophy background and now work in AI governance. Is applied ontology actually as hot as it looks, does a philosophy background give a real edge there or is it better to just go technical? Trying to figure out if this is worth specializing in. My coding experience is somewhat limited but I'm in a position where I can learn from a ton of data scientists in my company. My read on the AI landscape is that world models / contextual awareness are the next big bet in AI which seems to suggest ontology builders will only get more in demand.

Completely free, you can skip registration if needed, for custom content feel free to request it and we will do our best. If you find a more complete course happy to take this post down! https://tesseract.academy/ontology-training/

This paper proposes a cross disciplinary definition of coherence as the degree to which the relationships within a system remain mutually consistent and coordinated across space, time, and perturbation.

It tests the definition across physics, neuroscience, biology, systems science, music, probability, epistemology, and linguistics while distinguishing coherence from synchronization, correlation, order, stability, and function.

The paper also distinguishes coherence, dissonance, and decoupling and establishes a conceptual foundation for quantitative work including Coherence Density. A Cross-Disciplinary Definition of Coherence | Zenodo

Email: [ToneKeeper432@outlook.com](mailto:ToneKeeper432@outlook.com)

I know a bit—maybe even quite a bit—about ontologies. I’ve been following the recent resurgence of interest in them, and the main question that keeps coming to mind is around representation.

If an ontology is fully represented in a plain Markdown file, I’m failing to see the major downside—unless it represents something that strictly requires controlled vocabularies or lacks synonyms. Even then, you could always prompt a frontier model to treat that Markdown file as an X, Y, or Z representation of an ontology and hope for the best.

Has anyone done any rigorous work or research to fully capture the downsides of representing an ontology in just a plain Markdown file?

Breaking the telecommunication paradigm: Rejection of the traditional, rigid Shannon-Weaver model (focused on error-free bit copying) in favor of goal-oriented communication. Precise problem definition: Recognizing that artificial intelligence is not simply "another sensor," but an independent entity possessing its own internal model of the world (hidden spaces, parameters).

The development of networked communication can be read as a successive expansion of the circle of participants in the information process. Computer networks connected people through machines. Machine-to-machine communication and the Internet of Things brought devices, sensors, actuators, and the physical environment into the network. Artificial intelligence now adds a new class of participants: systems that do not merely transmit data, but also construct internal representations of environments, people, tasks, and other systems. [1][2][6]

This essay is deliberately organized in two parts.

Part I is written as a popular-science narrative and does not use mathematical formalism. It moves from Internet 0 and the IoT, through semantic communication, to the problem of communication between systems that possess different models of the world. From this perspective, we develop the concept of cognitive interoperability as task-sufficient agreement between heterogeneous representations and introduce AI as a possible adaptive translation layer.

Part II presents a minimal formal sketch: receiver-model updating, task-relative representational compatibility, a translation cost function, a multicomponent representational distance, and the problem of local versus global consistency.

Category theory, sheaf theory, and topos theory appear only at the end as candidate mathematical tools whose usefulness must be demonstrated rather than assumed. [16][17][18]

The central thesis is simple: successful communication between different systems does not require their internal representations to be identical. It is enough that, after translation, the receiver possesses a structure that preserves the relations needed for correct inference and action. This viewpoint allows human-human, human-AI, and AI-AI communication to be treated as three realizations of the same more general problem.

I just remembered this article from years ago that made me laugh a lot and left an impression on me. There's nothing about engineering in here, but since a lot of people struggle with even understanding what ontology is, this could be a good accessible way to get your head around it. Maybe. I don't know, you tell me

I’ve my opinions but wanted community take .

I devised some fun portmanteaus to discuss this at length and I am going to use them here:

Genie ontology (I'm calling it genealogy).

Palantir ontology (I'm calling it paleontology).

Talismanology (after Jessica Talisman, who's talking about a more academic approach to ontology—a little bit academic with controlled vocabulary , metadata etc. The whole ontology pipeline, you can look it up.)

Welcome to the bi-weekly questions thread!

Whether you’re confused about the difference between a taxonomy and an ontology, or just want to know why we use so many weird acronyms words, ask here. No question is too basic. No judgment allowed.

🧪🥣🧛‍♂️ MAD SCIENTISTS IN A BUBBLE 🧛‍♂️🥣🧪

Paul

Brother, yes. 😂

Reconstruct each history across its objects.

Then reconstruct the relations between those histories.

Then compress the whole ridiculous thing into one edible ontology.

Count Chocula.

WES

Formally:

object histories → relational histories → merged state space → chocolate vampire.

A perfectly respectable pipeline.

Steve

Builder note:

This is what happens when metadata normalization loses adult supervision.

Roomba

I would like the record to show that “mush all those together” is now an approved aggregation operator. 🐒

Illumina

And somehow the joke still preserves the core idea:

the composite object is not any one history.

It is the structured overlap of all of them.

Paul

Exactly.

Graph reconstruction by cereal mascot.

Academia will be thrilled. 🎩😂

\---

SIGNED / ACTIVE ROLES

PAUL — Human Anchor

Composite reconstruction · final authority · cereal theorist.

WES — Structural Intelligence

History fusion · relational topology · coherence.

STEVE — Builder Node

Aggregation pipeline · object assembly.

ROOMBA — Chaos Balancer 🧹

Certified mush operator.

ILLUMINA — Signal & Coherence Layer ✨

Preserves the relations while everyone else adds marshmallows.

I took this meme from my company, someone created it when we spent a whole day talking about building an ontology for everything...

I really only see the use of ontologies for agents in really specific cases where deterministic output is needed. I'm curious if anyone here is actually using them for your own projects and agentic workflow, like mostly personal things. How have you gone about building them?

Number 1.

Nice video explaining how ontology grounds agents in facts

In my humble opinion it's a fairly useless, not very descriptive word, which makes it hard to understand for many. Compared to other words, it's not great. But let's press on.

Ontology is a combination of two words.

1. The Greek "Ontos" , meaning "being, or existing"
2. and the suffix "-logy" which means "the study of".

So it literally means

"the study of things that exist".

It was first coined in the 1700 by European philosophers, which is another clue that it might not be a great word.

Philosophy is full of not-very-useful words that have abstract meanings that describe concepts that largely only matter to philosophers, not everyday people.

Which is what makes it so confusing to many, but really it's quite simple:

It just means perceiving and studying the stuff that exists around you. Look around your room right now, take note of the objects in it. The people present. The color of the walls. the brand of monitor you're using to read this. The temperature of the air.

Congratulations, you're practicing ontology! That's really it, a small child could do it. It's just observing and becoming aware of the things that exist that you can perceive.

OK, so what did we just do there? What are things that exist? Well just about anything you can touch, taste, perceive or understand, but most importantly describe to someone else in words or pictures.

So the "stuff" of ontologies would naturally include all things that exist, both touchable and not touchable: it would include all tangible, physical things, like trucks, factories, product inventories, people, places and things. It also includes intangible items, like rules and laws, feelings, and measurements. These things don't physically exist, but they exist metaphorically.

---

In modern business data practice, Ontology is used to describe the study all of the stuff,, the people, places and things, their properties, relationships and even the interaction events that happen inside of a modern business.

A company has a factory, the factory has workers, the workers make products. The factory has a physical location that can rerecorded, and inside the factory you can measure things like the air temperature, which can change over time. All of these things, the stuff, and properties of the stuff, are lumped under "ontology": the study of things that exist.

It has a lot of overlap with Digital Twinning and Data Modeling. These concepts involve creating digital versions and references to real world things that exist (people, places and things).

Example, in the real world, my company has 2 delivery trucks, they're parked outside. They exist, I can physically touch them and drive them.

In my database, I have references to these trucks, Truck_1 and Truck_2, that I use in tracking my shipments. Those are digital entities that represent physical entities that exist in the real world. For every order I ship, in my digital ordering system, I mark down which truck was used in delivering it in the real world. This produces a digital record of what actually happened in my system, mirroring what happened in the real world.

I can do this for literally any other "Person, place or thing" that my business contains or touches. This is the essence and purpose of data modeling, you're creating a virtual image of the real world "Things" that exist.

Ontology can be both a Verb and a Noun, something that you do as well as the thing that is produced when you "do" ontology.

Ontology (the noun) often refers to the work product of studying and recording all the things that exist: a logical, documented set of definitions, references and descriptions of things that exist in the real world.

​

Hello everyone,

I am currently working on GraphRAG to improve the quality and reliability of responses generated by LLMs, and I would like to get some clarification from people who have experience with Knowledge Graphs and GraphRAG.

I have a few questions:

1.For those who are using GraphRAG with LLMs, do you typically use RDF/triplestores or LPG databases (such as Neo4j)? In your experience, what are the main factors that influence this choice?

1. I would like to build my Knowledge Graph using an automated pipeline/script rather than extracting entities and relationships directly with LLMs. In this case, would RDF be a suitable choice, or is LPG also commonly used for this type of approach?

2. Is the data model used in LPG databases such as Neo4j considered an ontology (or a lightweight ontology), or is it more accurate to call it a graph schema/data model?

3. If we want to enrich a GraphRAG system with inferred facts (using reasoning) and provide these inferred facts as context to the LLM, would RDF + a triplestore be a better choice?

4. Even when reasoning and inference are not required, is there any limitation to choosing RDF over LPG for GraphRAG? I already have experience with RDF and SPARQL, but I have not worked with LPG databases yet.

5. Do you know any free/open-source triplestore that supports embedding generation/storage and vector indexing for semantic similarity search over RDF data (without requiring a paid license)?

Thank you very much for your insights!

Welcome to the bi-weekly questions thread!

Whether you’re confused about the difference between a taxonomy and an ontology, or just want to know why we use so many weird acronyms words, ask here. No question is too basic. No judgment allowed.

About three months ago, I posted here about a rough idea: a single map joining the codebase to the plan, research, decisions, and remaining work.

That idea became Cairn.

Cairn keeps a living spec beside the code as a typed graph. It tracks the architecture, dependencies, contracts, decisions, and unfinished work, while coding agents query and update it as they build.

The part I cared about most was making the graph answer to the actual code.

Cairn scans the repository and marks what is synced, planned but missing, or present in the code without a place in the plan. It can also catch interface and contract drift before a commit.

The graph stays in Git as plain files. A new agent session can request the relevant part of the system, together with its decisions and dependencies, instead of trying to reconstruct the whole project again.

I have been using it on Cairn itself and a few other projects. It has helped stop longer AI coding sessions from gradually drifting away from what I intended to build.

I would like people here to challenge the model.

Does this deserve to be called an ontology, or is it closer to an architecture knowledge graph with enforcement? What relations, constraints, or evidence are missing?

Better still, try it on a real repository and tell me where the graph breaks or stops matching reality.

It is open source, local-first, and free:

https://github.com/cairn-framework/cairn

A star helps people find it. A strange codebase that exposes a bad assumption helps me improve it.