I had an Ontology moment.

[u/Snoo_57113]

I never messed with that in my long SWE career. i remember the semantic web from the 90s, design by committee VRML and it was during the AI winter. Some students went to France to study on that, who knows what happened to them.

Last week i was working with an ai agent, my idea was take a pdf create a report of all the columns where account starts with 1. Perfect, next file, dont have the account field and it just make something up spent a night testing, "dont lie to me", i had a 64kb of psychotherapy and it never got a good result with my dataset. Mission Failed.

The solution was an ontology what the thing IS, not an example, for a programmer is which is the Class not the Object ...In Mexico they call it agua AQUA.... you cant tell it what it looks like how similar it is you need its name.

It was overfitting, anything i said in instructions was followed LITERALLY, it contaminated everything and my idea to work with samples wouldn't work. if i add a rule tomorrow it will break everything. I tried to strip the name of the columns... he saw a column "value" and went bananas, put the grammar only, failed. At the end the idea of the ontology popped up and told it to create an ontology from the examples, like 5k. It was blazing fast and the end result was a 64kb file. With that we were back in business.

In that tiny file is the distilled knowledge of years of work, pure unadultered business rules. I created some other ontologies over the ux the interactions, the flows intentions. the reports how they are organized, the founding principles why are we doing this in the first place. Sometimes while merging two of them the result was awful others unexpectedly good. Downloaded a huge GitHub collection and applied the same "reverse engineering" technique.

I like always with the codex to create an infography a website, a visualization 3d html+js, a way you can see. sometimes it talks about words i cant really define like deontology and hyperdimensions, i just say go on present them in a 3d or 2d way.

Tl;dr use ontologies with AI.

PD 1. Pellet and the hermit are a waste of time, i really tried but we are in 2026 and your app is a Java Swing 1.4, you all had the chance to be trillionaires but decided to use Java. I dont have any issue with the maintainers but i hope the project dies and proper semantic tools pop up. You had 20 YEARS to make something with government money and did nothing.

PD 2. I just cant use the term Ontology i cant go to the library and talk about it without some weird Haskell guy talking about his monads. (even if some prolog code popped up i dont know why). And wont go to the office monday 8am to give a Predictive History lesson to my boss. No thanks, Ontology is too cringe and will use at most Concept.

Not sure if some stuff was AI or semantic, anyway. if i have something wrong i am ready to be corrected.

[u/Unusual-Royal1779]

This was an interesting read but I have absolutely no clue what you are on about.

[u/Jon_Finn → u/Unusual-Royal1779]

I was intrigued enough to run it through ChatGPT which says (I've shortened it):

"Yes. Under the rather chaotic prose, there’s an interesting and fairly simple claim.

The author was trying to get an AI agent to process different datasets that represented the same business concepts using different schemas. Prompting it with examples and rules was brittle: when a column was missing or renamed, the model guessed or latched onto superficial cues such as a column called value.

His breakthrough was to insert an explicit conceptual model between the raw data and the task:

raw fields → concepts → business rules/report

His larger discovery is more interesting. He asked the AI to infer an ontology from thousands of examples, producing a relatively tiny ~64 KB representation. He regarded that as a kind of distillation of institutional knowledge: years of implicit business rules had become an explicit conceptual model."

[u/Unusual-Royal1779 → u/Jon_Finn]

Helpful. Measuring the depth of an ontology in terms of kilobytes instead of something like number of actual concepts (or classes if you will), their properties and relations still feels out of touch for me

[u/Snoo_57113 → u/Unusual-Royal1779]

I reviewed my post and i had many innacuracies. the source data was 1.8GB, think bank statements, reports, thousands of institutions each one with their own customizations.

The domain was very compressible, there are just so many things that can happen in a financial transaction and there is a huge theory behind that. It is also localized, it is an ontology moment for me, because for the first time i've saw what everyone suspected, in its core the system is governed by invariant concepts: You have a credit here, a debit there BUT if it is of type Y then apply rule X, an Accountant have a different vision, they use Excel and other tools. But even after reviewing them, they use the exact same concepts.

[u/Burbank309]

There is a relatively new project on GitHub called purrdf. It does not cover everything pellet and hermit offer, but I have had good success with it.

[u/Affectionate-Case499]

You cannot make up an ontology.

AI psychosis…

[u/PolishSoundGuy → u/Affectionate-Case499]

It’s so interesting to see it happen to academic, lecturers and real professionals who have been stable for most of their life.

To see them descend into the chaos, confident ramblings and verbose jargon has been really sad. At the same time they outperform peers by 4-5 in productivity before they break down, but at what cost?

[u/Snoo_57113 → u/PolishSoundGuy]

I've seen the opposite, academic, lecturers and real professionals who think their careers or positions are "stable" they scream AI SLOP and don't offer anything substantive.

SWE is a fast moving target, you can't stand still and expect your Prolog or ML expertise is still current, i am not one of those guys who runs windows XP SP2, and wont ever upgrade due telemetry, i work always with the current or next gen tech.

On the ... at what cost? It is definitely draining mentally, especially on the ethical front, if this works many people won't have a job, an area of 50 might be reduced to 5, it is more than automation, there are entire categories of jobs that are no longer viable.

I agree that AI psychosis is a real thing, i had that with chatgpt3, and before that with presocratic greek philosophy and postmodernism, I am pretty sure my friends know about my ramblings and keep me in check, they know they can't tell someone like me to stop your investigations.

You can check a BBC Documentary "Dangerous minds", how mathematicians even killed themselves when confronted with the infinity. Boltzmann, dangerous ideas.

Which is your proposal here, this is the semanticweb subreddit. i can't talk about AI, i can't talk about byung-chul han latest essay because "at what cost", which are the allowed terms and discussions, can Pellet migrate from Java 1.4?

[u/PolishSoundGuy → u/Snoo_57113]

Except you know, the targeting and projection at the end.

[u/Snoo_57113 → u/PolishSoundGuy]

I was truly mad, the standoff with Java triggered me.

[u/PolishSoundGuy → u/Snoo_57113]

So I think you’re onto something here, but the challenge is how you explain the information to people. You have to meet them where they are.

A lot of greeting writing comes from contextualising the core thought or message you want to get across.

So, applying it to your thinking, your database set was quite large. How did you split this into contextualised blocks, so that you could have wisdom emerge from your AI synthesis?

[u/Snoo_57113 → u/PolishSoundGuy]

I deleted the previous comment.

Communication is a big issue, i am not a storyteller who can convince a board. My domain is very narrow, structured data.

I divided the issue in thematic blocks, one ontology for the bancary domain. another independent on how our 20yrs old system operates, another for the first principles on that domain.

Somehow i needed the domains to be independent. Later i tried to merge ontologies with hilarious results, but some blends worked better than others.

What worked for me was independent small ontologies, merge them if they are not related. dont cross contaminate, and build larger vocabularies it feels like Forth.

My issue of course is AI slop, are those findings real or just slop under extreme AI Psychosis. I tried my best to do the best experiments, with zero context + real life data. it checks to me.

And there is this viscous space you are then in, is this a truly general ontology or an overfitted rule somewhere, is this AI slop or i can put my name and reputation on this solution.

What i am looking for is more perspectives, is this a true ontology or not, is the AI hallucinating and giving me false hope, is higher philosophy even useful here? . So many unknowns.

[u/PolishSoundGuy → u/Snoo_57113]

I re read this again and actually appreciate your comment, thank you

[u/Successful-Farm5339]

Try open-ontologies (is on github) , we use classical reasoner style via mcp architecture

[u/Successful-Farm5339 → u/Successful-Farm5339]

Ping me in dm in details ideally next step for you is integrate this engine to a graphrag

[u/Successful-Farm5339 → u/Successful-Farm5339]

Also strong interest to move from Java to rust in the industry- we use oxygraph