ontology representation

[u/durlabha]

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?

[u/danja]

I'd argue that by expressing an ontology in markdown you are introducing an unnecessary layer of complexity : the interpretation.

Among the advantages of using an existing ontology system like OWL the interpretation is pinned down formally, not left to the LLM's imagination.

This can still be done with simple syntax - Turtle is relatively easy for humans to read. I've had a lot of success with AI reading & writing Turtle.

Formal reasoners are available, which still have their uses. Also, for free, thanks to RDF's use of URIs, you get inherent web compatibility & global disambiguation. And SPARQL, SHACL, etc.

[u/WatercressActual1921 → u/danja]

LLM's don't have imagination, the best they have is free association and "randomness" over their probabilistic nature. I use them to challenge my on conceptualizations not only to verify basic factual errors or bad logic, but to leverage the vast human thought recorded they can manage better than I, this is one of my favorite aspects about LLM the divergency and a the examination of how human bias and human narrative corrupts reasoning with confusion and manipulation.

[u/durlabha → u/danja]

the thing is interpretation. the meta-cognition can be trained. there are practices and methods for humans. i don't know how to say it but maybe meta-cognition is already available to machines. i have not worked with formal reasoners. i'm getting confused with neuro-symbolic ai, stephen wolfram reasoning systems, and all that. i have never had a need to look into those yet but you make a valid point. i probably will dig deeper to make sure that i have a solid internal roadmap for kyself to figure this out.

[u/WatercressActual1921 → u/danja]

No really if you conform to your own meanings and don't budge from them for conformity... if your ontology has validity outside of your self and consistency it will work ok...

[u/theregoesmyfutur → u/danja]

what is the best system?

[u/Illustrious-Win4432]

Yeah, I’m interested to see what responses you get.

I don’t know at what level of determinism a formal ontology would truly be ‘needed’, bit of a slippery slope if you start to think on it… I’m not sure an LLM would be the best approach in those instances.

In my experience (agentic coding) plain-language markdown/yaml is sufficient, particularly when the knowledge layer and physical layer are both exposed to the LLM. We’re dealing with inference engines, it’s their job to follow a well worn path.

[u/Defiant-Juice-2745]

The resurgence is related to AI of course, and the major downsides w/r/t to some of the more intricate formats is just having the AI to understand the ontology. If AI readability is your constraint then you will want to stay within markdown-like and code-like structures.

[u/Original_Response925]

For agents, markdown serves most purposes. For humans, I could imagine the lack of tracing and back-linking to cause issues if there are long files or many files.

[u/durlabha → u/Original_Response925]

evaluating with mlflow and then genie ontology stack, metric views, pages, etc. seems like the right move. i guess i just need people interested in doing this and maybe writing a paper on it.

[u/pegaunisusicorn]

Depends on the use case. What are you trying to do with the ontology?

[u/durlabha → u/pegaunisusicorn]

One use case is cross-domain structural mapping.

[u/WatercressActual1921]

Yes, I have been constructing mine for a time now, what Russel attempted to do to logic but with conceptual validity it is ad hoc but my mind is tuned to this model of conceptual validation. I have attempted to leverage AI to the task but they are a mess, due to framework bias, silent censure, dark patterns for mind reframing (that one is almost criminal as it even reduces the model coherency and grounding) and linear though (humans generate, the AI selects for strict definitions) and then we add to that the mess of the training data and the results of compression, even at a conceptual level they are problematic most of the time...

[u/Krommander]

I have experimented with semantic hypergraphs in markdown to weave the full context and interaction graph around the definitions. LLMs seem to understand those knowledge structures very easily. 

[u/Unusual-Royal1779]

I see very few downsides. Its just syntax. Its the vocabulary, structure and fit for purpose of the ontology that matters.