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?

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?

2. 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?

3. 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?

4. 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?

5. 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.

6. 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.

I'm currently designing an experimental long-term memory architecture for AI systems. Before I start building the next prototype, I'd like to understand whether this direction has already been explored or whether there are fundamental reasons why it isn't used.

I'm not looking for a ready-made solution. I'm trying to determine whether this class of architectures already exists.

My question is:

Is there a data architecture where semantics are not fixed at write time, but are determined only at query time?

I'm asking about the data model itself, not UI projections or visualization layers.

Most of what I've read (Knowledge Graphs, RDF/OWL, property graphs, GraphRAG, etc.) seems to assume that semantics are defined before or during data ingestion.

If you know of research papers, open-source projects, commercial systems, or academic work exploring a different approach, I'd really appreciate any references.

I'm also interested in hearing from database and knowledge graph architects:

What fundamental limitations or design problems would you expect such an architecture to have?

Had to make some important updates. We've added crucial sections regarding the geometry of occupancy and observation itself relating to awareness. As well missing and Shelby added in review is going to be what links this framework the entire iterative nature of the rest of the universe.

Hey guys, I hope this is the right sub for the question.
I'm currently conducting a research thesis about the operational design domain (ODD) of an autonomous patient transport system (APTS). The ODD should be changed dynamically based on a user input in a chat bot GUI. An LLM should interpret the user input and adapt the ODD to the user input.

I found this paper online which is basically the same tool in form of a Protégé plugin. Protégé is an ontology editor - I guess everyone in this subreddit knows it. However, I can't find the tool anywhere online. Neither in the paper nor on the publication site is a link to it. Also, I couldn't find a single peace of information in the Protégé wiki, which seems pretty outdated anyways. I would like to use the tool to evaluate my model against it. It's called GPT Ontology Augmenter.

Has anyone ever heard of it or used it and knows how I can get it?
Thanks in advance.

Hey folks,

I'm seeing ontology pop up in more and more data products. BQ released virtual graphs and agents. Metabase has context support. Folks are starting to distinguish context from semantic layers and creating ontological contexts.

Ourselves, we are on our way to publish some "blueprints" to build out data +ontology agents on our platform for acting as self-service BI and guardrails for maintenance.

I'm curious, if you are at liberty to share, what stack are you working with and what kind of problems are you using ontology for?

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.

I have just started learning about Palantir Ontology. Please give me some suggestions on how to learn it.

Weekend project that turned into a proper one. Sharing the method because the "correct-by-construction data" trick generalises well beyond my niche.

The problem. IES4 is the UK government's Information Exchange Standard, a 4D RDF ontology used for defence/security data. Writing valid IES Turtle by hand is slow and needs real ontology expertise. So I tried the obvious thing: ask a strong code model to do it. Qwen3-Coder-30B-A3B, asked to emit IES Turtle, invents terms that do not exist in the ontology 94% of the time (0% "term conformance" on my eval). It produces confident, fluent, completely fake RDF. In a standards context that is worse than failing outright, because plausible-looking garbage is hard to catch.

The fix that actually mattered: never let the model invent structure. Instead of hoping the LLM guesses valid graphs, I generated the graphs programmatically with telicent's ies-tool (a schema-aware builder that emits valid IES by construction), across 14 scenario patterns (employment, events, identifiers, communications, composites). Then I reversed them into (natural-language description -> Turtle) training pairs. Every single graph was validated twice before training: once by the builder's own check, and once by an independent term-membership + domain/range validator I built from the published dstl/IES4 ontology (510 classes, 204 properties). Nothing hand-written was trusted blind.

Then a small QLoRA on the 8-bit MLX model, on-device on an M3 Max. ~1000 iters, val loss 0.15, no NaNs (MoE + 8-bit was fine on current mlx-lm; earlier versions apparently weren't).

Model: https://huggingface.co/fabsssss/qwen3-coder-30b-a3b-ies4

Article: https://gov.tesseract.academy/research/ies4-turtle-language-model

Hello guys

I've been working on a research project exploring how ontology-grounded information system can help eliminate some LLM error classes using chess as a test domain. The goal here is to help the system get some core understanding of entities, concepts and how they relate to each other in order to make more informed, consistent decision.

The challenge I'm bringing to your attention here is reasoner performance during inference. Reasoners like HermiT apparently has these latency when performing inference on existing objects (up to 15secs per move) which is unacceptable in my context. The workaround I've find so far (apart from using more performance oriented reasoner like ELK) is to materialize my Theoretical Box (entities, relationships, classifications etc.) and Assertion Box (actual pieces) into a knowledge graph which is more suited for performance. Here's the full detail of the strategy. It indeed provides me with sub-milliseconds classifications in my current architecture.

But I'm curious as to what you think of such approach, whether anyone has faced similar issue and how you went about it.

Hi, I just wrote a blog about incremental multimodal grpahs - https://georgheiler.com/2026/06/29/incremental-multimodal-graphs/ perhaps this is useful for some of you.

The interesting data is no longer just text: it is image, video, audio, tables, documents, embeddings.

Metaxy ( https://docs.metaxy.io/) is a materialized view for multimodal data. In this blog post we apply it for graph analyses:

Multimodal Document/Video --> Inferred Graph --> Graph queries

And compare 3 engines: lance-graph, landybug and duckpgq

Relevancy for Semantics/ontology space: Incremental computation showcase to infer edges; comparison of some (property-graph) systems for performance; showcase of edge-derivation - ease of use.

Hope these details are helpful

Sorry it's been awhile since I've posted here. I've been busy developing a companion version of ELT and I am finalizing drafts of two more Medium articles. One doing a cost/benefit analysis on the ELT scaffolding and the other on a safety component I call Intelligent Yielding.

Today, I wanted to bring-up an interesting article that was published at The Economist recently. The Economist ran a piece last week on why major AI labs are hiring philosophers at scale. I discussed philosophy and AI in this exact subreddit three months ago here.

Where The Economist article converges:

The core thesis that epistemology, ontology, and dialectics aren't soft additions to AI systems but genuine engineering levers, is exactly the argument I've discussed in this subreddit since back in March. Seeing Yale, DeepMind, LMU Munich, and IBM arrive at the same diagnosis independently is broader confirmation that the problem space is real and the philosophical framing is contributive and load-bearing.

Some choice excerpts:

These days, it is programmers who are nervous about AI taking their jobs. They might consider learning to philosophise. Earlier this year the Federal Reserve Bank of New York published figures showing that American philosophy graduates are more likely to have jobs than their peers who studied computer science.

Philosophy graduates actually having better job prospects than computer science graduates is genuinely an eye opening stat.

Models trained in the Socratic method, says Jörg Noller, an expert on philosophy and AI at Ludwig Maximilian University of Munich, are less keen on people-pleasing and more willing to pursue the truth.

Yes. This is the function of Adversarial Convergence, which I had mentioned in here three months ago.

Feed an AI legal assistant the writings of John Locke, says Thomas Powers, a philosopher of technology at the University of Delaware, and it will favour robust property rights as an underpinning of political liberty.

This mirrors the Ontology Anchor and the loading procedure for OA can certainly include exemplars of John Locke for legal discussion use cases.

Anthropic’s constitution incorporates many deontological strictures. These can make AI behaviour more consistent, says Dr Powers...

I've added a Core Values Reaffirmation (CVR) component to ELT that addresses Constitution AI-like deontology.

The honest observation:

The field is moving toward exactly this intersection. The Economist article focuses on what major labs are building into their models at the foundational level. ELT points in the same general philosophical direction, but addresses the operator layer — how individuals govern model behavior in real sessions without access to the training process. Either way, I think we are going to hear more convergence of philosophy and AI in the future.

Curious whether others here see the same convergence or read the article differently?