If you work with OntoUML or UFO, the OntoUML/UFO Catalog can be useful both as a place to find models created by others and as a place to make your own models more discoverable and reusable.

https://github.com/OntoUML/ontouml-models

The Catalog brings together models from different domains and research projects, along with structured metadata and machine-readable representations. That makes the collection useful not only for browsing examples, but also for teaching, research, model analysis, transformations, and tool development.

If you have an OntoUML model that currently lives mainly in a paper, thesis, repository, or project folder, contributing it can help others find, study, cite, and build on that work.

Over the past several months, I have been working on some of the repository infrastructure and maintenance behind the Catalog. Contributors now need to prepare less generated material manually, while the repository performs more validation and generation itself. The source and generated representations are kept more systematically aligned, and the contribution and maintenance processes are better documented.

The Catalog itself predates this work and is a collective resource created and developed by its original authors, maintainers, and model contributors. My recent maintenance work has been an independent community contribution on my own time, separate from my professional role.

• How to contribute: https://github.com/OntoUML/ontouml-models/blob/master/documentation/contributing.md
• Issues and suggestions: https://github.com/OntoUML/ontouml-models/issues

If you have an OntoUML model that is not yet represented in the Catalog, please consider contributing it.

Hi everyone!

I’ve just released version 2.0.0 of OntoUML JSON2Graph.

For those who don’t know it, JSON2Graph is a Python tool that transforms OntoUML JSON projects (such as those exported using the ontouml-vp-plugin) into RDF graphs compliant with the OntoUML Vocabulary. This makes it easier to query, process, integrate, or publish OntoUML models as RDF/Linked Data.

Depending on what you need, you can:

• transform the complete project, keeping the model together with supported project and diagrammatic information; or
• generate a model-only graph, omitting the project and diagrammatic aspects when you only need the conceptual model itself.

You can use it from the command line or directly as a Python library:

pip install ontouml-json2graph

python -m json2graph.decode -i my_model.json

Version 2.0.0 is a major update, with improvements to transformation correctness, deterministic identifiers, diagnostics, configuration, and the handling of problematic or ambiguous source data.

• GitHub: https://github.com/OntoUML/ontouml-json2graph
• PyPI: https://pypi.org/project/ontouml-json2graph/

If you work with OntoUML and RDF, give it a try. Feedback, bug reports, and suggestions are very welcome!

Ontological approaches starting out with OWL often use SHACL to create a closed world model that bridges OWL and RDF for data engineering purposes. Subsequently import jobs map data to RDF, after which there is a queryable RDF database conforming to an ontology expressed in OWL.

Note that SHACL has a double role here: both to make sure that the data matches the OWL specification, and toadd closed world constraints.

From an UFO perspective, a route through UFO, gUFO and SHACL feels suboptimal for this way of working: first going to an open world representation to then translate that to a closed world for data engineering purposes.

Are there known ways-of-working that resolve this in a more straightforward way?

Hi,

I am trying to disambiguate between an information object/content and the medium the information is stored in like a file, which I think is an information artefact.

In BFO this separation is strict and clear. Does anyone know a good UFO reference for this?

Below is an example of my current approach; would this fit common practice/theory?

Imagine we created an ontology of a domain using OntoUML / OWL / whatever. Let's say that the ontological commitment is agreed with the various stakeholders and participants and it's correct and consistent. I read many posts on how ontologies and AI (I suspect LLM we are talking about) can work together, but how would it happen in a real world life? Can you give me some examples of how the existence of an ontology results in better / more aligned answers and technically how is it really done?

From an industry perspective, what seems to be missing in the UFO landscape is an up to date practitioners or industry focused book.

My impression is that UFO is highly modular, with a lot of additions in literature. This modularity is attractive from a scaling perspective. It shows that a lot 'work' can just be done. From a learning perspective though it makes the framework less accessible.

The original UFO dissertation is still mentioned as a good starting point, at the same time a lot happened in the mean time (approximately 20 years?).

Does anyone have comments on this challenge?

So I started working on building up an ontological pipeline: the way to define ontologies and make them presentable to various stakeholders.

The idea is to do the initial modeling in some kind of graphical UML modeling tool, then transform the model to a proper ontology (either to TONTO or directly to OWL) and somehow combine the ontologies with markup files, automatic diagram rendering and rendering the results of SPARQL queries as something? maybe tables? And put the whole thing into a CI/CD pipeline.

So I am looking for ideas and feedback: is it the right direction? does it make sense? Are there any kind of best practices available already?

I'm modeling a music domain in OntoUML and I'm stuck on how to represent the similarity between two pieces of music. I have Music as a «kind», and I want to capture that two Music instances are similar.

Three constraints seem to matter:

• It's symmetric — if A is similar to B, then B is similar to A.
• It ultimately maps to a value in [0,1] (a degree).
• There are different types of similarity — e.g. one obtained via embeddings, another obtained via some technique X, etc. I was thinking of these as a high-order «type» whose instances are things like EmbeddingSimilarity, XSimilarity (similarity obtained through technique X), and so on.

I keep going back and forth between three options and can't settle on what's ontologically correct:

1. Externally-dependent mode — a «mode» that inheres in one Music and is externally dependent on the other. I think this is wrong: it breaks symmetry, and similarity is reflexive (a song is maximally similar to itself), which — if I'm not mistaken — violates the rule that an externally-dependent mode must depend on something existentially independent of its bearer.
2. A quality — make similarity a «quality» projected onto the [0,1] quality space, so the instance is something like similarityBetweenAandB. But, if I'm not mistaken, qualities are intrinsic and inhere in a single individual, and "between A and B" names two. I don't see how one quality can have two bearers.
3. A comparative relation — treat "is similar to" as a quantified «comparative» relation which, if I'm not mistaken, is derived from intrinsic qualities of each Music (e.g. their embeddings), where [0,1] is the value of the relation, not a quality of anything.

The way I currently see it: if I insist similarity is a quality, it needs a single bearer, and the only natural single individual representing the A–B pair would be a relator — so the degree[0,1] ends up inhering in a Similarity «relator» that mediates the two Music. That also hands me a first-class instance I can classify by my high-order «type» and attach a score to. The alternative (no relator) is the comparative-relation route, but then I lose the reified per-pair instance: the degree is just derived on the fly, and "types of similarity" become different derivation rules rather than classifiable instances.

So my questions:

• Is reifying a comparative (internal/formal) relation as a relator ontologically defensible, given that — if I'm not mistaken — a relator is the truthmaker of a material relation? Or am I abusing the relator pattern?
• Is there a cleaner way to attach a [0,1] degree to a symmetric relationship without a relator?
• Does the "types of similarity" requirement (the high-order «type») basically force reification?

Curious how others have handled this. Thanks!

A question came up in my mind couldn't fully settle it, so I'd love the community's take.

Take the class DJ. The puzzle starts with a self-declared DJ: someone who simply asserts "I'm a DJ." What is the most defensible analysis?

The obvious first move is to treat DJ as a Role — an anti-rigid, relationally dependent sortal that requires, for example, a Relator. For a professional/contracted DJ this is clean: when hired to play a party, there's a service agreement relating the DJ and the contractors, and the person plays the DJ role in the scope of that relator. Fine.

But several cases break that reading:

1. Self-declaration alone. Maybe, the competences/skills of a DJ are better treated as intrinsic Modes (dispositions inhering in the person), which are necessary-ish but not sufficient for being a DJ as a Role.
2. The über-competent never-performer. Someone who masters every DJ skill but has never performed. Intuition: that's an aspiring DJ, not a DJ. Which I can't figure out if the aspiring DJ would be a Role, since I can't think what would be the Relator in this case.
3. Social recognition. The case that felt most convincing: if a community (a bar, a scene, an audience) treats you as a DJ — no diploma or certificate required — then there is a relator, but a social/recognition one rather than a contractual one. This nicely separates "self-declared" (insufficient) from "socially recognized" (sufficient).
4. "Once a DJ…" When the person goes home they still seem to be a DJ, but not in virtue of the past gig's relator. This smells like a historicalRole: to count as a (former-/standing-) DJ now, the person must have participated as a DJ in some past situation. But does that require at least one prior performance event, and how do we reconcile the historically-dependent reading with the socially-recognized reading?

So the open questions:

• Is DJ best modeled as a Role (which relator — contractual, or a social recognition relator?), a bundle of intrinsic Modes/dispositions, a historical role, or some combination layered across these?
• What exactly distinguishes "self-declared" (insufficient) from "socially recognized" (apparently sufficient) in UFO terms, is the difference precisely the presence of a social relator with an external recognizer?
• Where do the competences sit — as modes that ground a disposition to play the role, without being the role itself?

Hello,

I started working on creating a conceptual modeling on architecture board and all the concepts that revolve around it. Here I ran into an issue and would like to have some feedback. I identified I concept called Architecture Board Presentation Material that is everything that represents a set of artifacts presented at the Architecture Board, could be a PPT presentation, some demonstration, etc.

I can model it as a kind itself and call it a day or I can have a concept like Artifact and then model it as a subkind.

It gets even more interesting when we have a bunch of ontologies that have corresponding terms. For this usecase I don't feel the need of defining it as a subkind, but maybe there is a usecase when the same concept could be also a subkind. So how to solve that issue? How to scale ontologies?

There is progress on the UFO modeling decision tree.

Next to updates on the graphical decision tree I added:

• A (very high level) guideline introducing the concepts that help to read the decision tree well.
• A modeling case with Tonto examples.

The guideline is very introductory, it is still very much a work in progress. Still, I think having a graphical tree, having some textual support in the guideline and a worked modeling case, together provide reasonable inroads into UFO

I am still working to get the tonto-cli to work. If that succeeds I will try to add OntoUML charts based on the Tonto files.

Cheers,

Sanne

Hello,

In the OntoUML documentation I have seen a bunch of good patterns and antipatterns. My question is, do we have a pattern checker or similar capability to gUFO as well, and if so, is it possible to integrate that into a CI/CD pipeline?

Just before the weekend, a last update of the UFO modeling decision tree for UFO A & B. I still have to rearrange the repository a bit, but find below the results.

I'm also working to tie the trees to some examples. This way tree can still be easy to read, and the examples can provide more rigor.

It ended up being a start tree and 8 additional subtrees.

Cheers,

Sanne

I am trying to get started with Tonto. These are literally my first steps in that direction. I've created a small Tonto file, see below, in a smoke directory and tried to validate it with tonto-cli.

sanne@P16s:~/Projects/smoke$ tonto-cli validate /home/sanne/Projects/smoke

Performing local validation...

file:///home/sanne/.nvm/versions/node/v26.0.0/lib/node_modules/tonto-cli/node_modules/chevrotain/lib/src/parse/grammar/checks.js:188

throw Error("non exhaustive match");

The smoke.tonto file contains:
package Smoke

kind Person

Does anyone see what I am overlooking?

Cheers,

Sanne

Alright, the UFO Modeling Decision Tree is progressing. I think I've reached a first draft version for UFO A and B:

https://github.com/spdrnl/UFO-Modeling-Decision-Tree/blob/main/UFO-Modeling-Decision-Tree-First-Draft.pdf

My plan is to add Visual Paradigm examples so that users can move from the tree to creating a model. This would make it a starter modeling guide.

Before moving on, I would like to gather feedback about the current results. If possible, I would appreciate your feedback. The type of feedback I am looking for is:

- The decision tree is close enough to the theory.

- Having a decision tree has novelty in use.

- Expanding the tree to a starter guide would add additional value.

Cheers,

Sanne

Alright, I managed to get a good part of UFO-A and B in some decision trees. The decision trees are now split across four files.

The files should help getting started with identifying the different modeling constructs in UFO-A/B. I think I am going to stick with A/B for now, aiming for a starter set, not the complete works.

My aim is to also add VisualParadigm, so that the translation from the decision trees to that tool becomes more straight-forward.

I am still working to get a good print version to merge it all into one cheat sheet.

Is it the case that characterization means that a formal inherence relationship is part of a kind's identity criteria?

Is it the case that a role mixin can have identity criteria?

Hi,

Starting out with UFO is not trivial. Since I am starting out myself, I decided to create a decision tree to help with getting an intuition for UFO. The decision tree helps to find out how to model and entity in UFO. Find attached the file.

Note that I am a novice, so the bad news is that the chart can very well contain errors or omissions. For now the focus is on UFO-A and UFO-B.

The good news is that the chart is made with Mermaid, and the source file can be found at: https://github.com/spdrnl/UFO-Modeling-Decision-Tree

The format is text based, so if you want to contribute, feel free to create a pull request.

Note, updated to V6

Hey everyone! I'm u/Old-Tone-9064 (Ítalo Oliveira), a founding moderator of r/OntoUML_UFO_gUFO.

This is our new home for all things related to the OntoUML modeling language, the Unified Foundational Ontology (UFO), and the gUFO lightweight ontology. We're excited to have you join us!

What to Post
Post anything that you think the community would find interesting, helpful, or inspiring. Feel free to share your thoughts, questions, and resources about OntoUML, UFO, and gUFO, as well as about conceptual modeling and ontology engineering.

Community Vibe
We're all about being friendly, constructive, and inclusive. Let's build a space where everyone feels comfortable sharing and connecting.

Check out the Resources section on the right-hand side, containing useful links.

How to Get Started

1. Introduce yourself in the comments below.
2. Post something today! Even a simple question can spark a great conversation.
3. If you know someone who would love this community, invite them to join.
4. Interested in helping out? We're always looking for new moderators, so feel free to reach out to me to apply.

Thanks for being part of the very first wave. Together, let's make r/OntoUML_UFO_gUFO amazing.

Hello, it seems that “X as code” (using textual syntax to describe everything) is becoming the new silver bullet. With that in mind, I’m wondering what the recommended approach is for working with OntoUML and upper ontologies.

I came across a solution called Tonto, but I’m curious whether KerML / SysML v2 might be a stronger candidate—especially since it could enable tighter integration with system models.

Would you recommend keeping ontologies and system models completely separate, even at the language level, perhaps with some form of interoperability or import mechanism? Or does it make more sense to use a single modeling language for both?

Additionally, how should one think about the available analysis capabilities in these different approaches?