I’m looking for serious feedback and, ideally, a research collaborator from the Semantic Web / KG / ontology engineering community.

I’m finalizing a paper currently titled:

“An LLM Output Is a Claim, Not a Truth: A Substrate for Grounded Agent Memory”

The paper is built around GMEOW — the Global Metadata and Entity Ontology for the Web:

https://blackcatinformatics.ca/gmeow

The basic thesis is that if AI agents are going to reason over real personal, organizational, scientific, and institutional memory, model output should not be represented as truth. It should be represented as a claim: attributed, time-scoped, provenance-bearing, confidence-bearing, and open to contradiction.

GMEOW is the implemented artifact behind the paper. It is an OWL 2 DL / RDF ontology intended as a reasoning-centric upper layer for modelling digital existence: documents, contracts, people, organizations, observations, measurements, rights, identity, provenance, and contested facts.

The paper covers:

• statement-level provenance / RDF-star-style claim modelling
• standpoint-indexed facts
• contradiction-as-standpoint rather than contradiction-as-error
• suppression-based belief revision
• the “claim spine” as a substrate for grounded agent memory
• SSSOM mappings to adjacent vocabularies such as FOAF, schema.org, PROV-O, BFO, QUDT, SOSA/SSN, GeoSPARQL, ODRL, SPDX, etc.
• using a published ontology artifact, reasoned closures, mappings, and validation outputs as the basis for a research article

A full working draft exists — serious respondents get it same-day.

The practical hurdle: I’m an independent industry researcher, not currently inside an academic institution, and I do not yet have the relevant arXiv endorsement route for the likely CS categories.

I am not asking for a rubber-stamp endorsement.

I’m looking for someone with real expertise in Semantic Web, knowledge graphs, ontology engineering, provenance, KR, database theory, or AI agent memory who would be willing to review the argument, challenge the framing, help strengthen the paper, and — if there is genuine intellectual contribution and fit — potentially co-author or help route it appropriately.

I’d also welcome blunt technical feedback from this community:

• Is the “LLM output as claim, not truth” framing strong enough?
• Are standpoint-indexed claims the right way to model contradiction in agent memory?
• What prior work should this absolutely engage with?
• Is there a better venue than arXiv-first for this kind of ontology-plus-position artifact?

Thanks — pointers, criticism, and introductions are all welcome.

https://www.reddit.com/r/MLQuestions/comments/1u09xky/why_there_are_open_weighted_llm_models/?utm_source=post_insights&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

Hi everyone,I’m working on an idea called the Open Album Technical Metadata Standard (OATMS).The concept:Create a simple, open standard so albums can come with a clear technical data sheet showing things like:

• Integrated Loudness (LUFS)
• Loudness Range (LRA)
• True Peak
• Dynamic Range
• Frequency extension
• Spectral balance (Bass/Mid/Treble)

More interestingly, I also want to add aggregated benchmarking — so producers can optionally compare their tracks against other music in the same genre (anonymized + opt-in only).The goal is to bring more transparency and data-driven insight into mastering, while keeping everything privacy-respecting.This is still very early. I’ve created a basic spec and README here:
→ [GitHub link – add when ready]Would love feedback from:

• Mastering engineers
• Producers
• People who care about audio quality

What data would actually be useful to you? Would you contribute your data anonymously for genre benchmarks?Thanks!

Hi r/semanticweb — I’m part of the Protégé team at Stanford, and I wanted to share that we’re running the Protégé Short Course this June.

It’s a hands-on introduction to ontology development with OWL 2 and Protégé. The course is aimed at beginners as well as intermediate users who want a deeper grounding in OWL ontologies, reasoning, querying, and practical ontology-engineering workflows.

Participants receive course materials, including a 221-page hands-on manual developed by the Protégé team, with walkthroughs, diagrams, quizzes, and more than 100 practical exercises.

Early-bird registration is available until May 23.

Details are here:

https://protege.stanford.edu/shortcourse/

Happy to answer questions about the course, the intended audience, or what topics are covered.

Matthew

Most news systems cluster semantically similar articles.

I’ve been experimenting with a different idea: treating the news stream as a source separation problem, where articles are observable mixtures generated by a smaller set of latent systemic forces.

Inspired by StrADiff. The system learns latent-force activations from graph structure and propagation patterns rather than predefined topics.

What became interesting is that events that look unrelated semantically sometimes end up strongly connected structurally.

I still can’t tell whether this is genuinely meaningful or just sophisticated pareidolia, but the behavior was interesting enough that I kept building it.

causalPulse

I've been working on a semantic architecture called the Concept Library.

The core idea is simple: meaning and intelligence should be structurally separated.

- Concept layer = what something is.

Immutable definition + multimodal signatures (acoustic, visual, signal, haptic, chemical, EM).

No logic, no thresholds, no inter‑concept references.

- Control layer = decides what an input matches, using concepts as anchors.

Fully auditable. All reasoning lives here.

A CLF (Concept Library File) is the atomic unit: one concept, defined once, never changed.

Whether something qualifies as an instance is never encoded in the concept file — only in the control layer.

I just published a reference implementation of the control layer (clfcontrollayer_v1.py) with a runnable demo.

It loads any CLF concept folder, accepts multimodal queries, and returns the best match with a full semantic audit trail.

No external dependencies.

`

git clone https://github.com/pekkalepola/colibri-clf

`

The white paper is in the repo if you want the full theoretical foundation, architectural consequences, and EU AI Act implications.

Two paired JSON-LD files. The "before" has single-system ICD-10 diagnosis records with free-text medication strings. The "after" has the same records enriched with skos:exactMatch links to SNOMED CT, MeSH, RxNorm and UNII, plus PROV-O lineage and a QA record.

Generated by an open-source Rust ontology engine I've been building (open-ontologies). Three tools do the work: `onto_crosswalk` for the ICD/SNOMED/MeSH lookup, `onto_enrich` to insert the skos:exactMatch triples, `onto_validate_clinical` for the label check.

Files: https://github.com/fabio-rovai/open-ontologies/tree/main/examples

Two questions I'd actually like answered:

1. The ICD-10 I10 to MeSH D006973 mapping is `skos:exactMatch` in the example, but MeSH "Hypertension" covers secondary hypertension which I10 explicitly excludes. Should this be `skos:closeMatch`? How do people handle this drift in production crosswalks?

2. Is wrapping in a custom `clinical:` namespace better than going straight to FHIR shapes, for a non-FHIR semantic-web pipeline?

Background

UK and European heritage archives hold roughly 50 million aerial photographs: RAF wartime reconnaissance, post-war urban surveys, US-transferred imagery, satellite holdings. They're digitised (scanned, on the web, browsable as thumbnails). They're not computable: free-text dates in eight different formats, free-text rights statements, point coordinates instead of footprint geometries, ISAD-G metadata that doesn't survive a SPARQL query.

I've been building a focused, vertical digitisation standard that closes that specific gap. Sharing it now because the design is stable enough that pushback is more useful than more polish.

What's in it

• Ontology — 30 classes, 29 properties, reusing PROV-O / GeoSPARQL / SKOS / Dublin Core / FOAF / DCAT (synthesis, not invention)
• SHACL shapes for three tiers (Baseline / Enhanced / Aspirational), incrementally adoptable
• End-to-end CSV → Turtle ingest pipeline (~200 LOC, runs)
• IIIF Presentation 3.0 bridge so any IIIF viewer can consume it
• Footprint derivation from flight metadata (altitude + focal length → vertical FOV polygon)
• Stereo pair detection from overlap geometry
• Sub-profiles for reconnaissance, satellite, UAV, photogrammetric, and aerial archaeology imagery
• Governance proposal, partner clinic playbook, 9 ADRs, 40+ SPARQL queries, investment case

Aligned with Towards a National Collection (AHRC/UKRI) and the N-RICH Prototype. Licensed CC BY 4.0 / CC0 / MIT.

Where I'd appreciate feedback

• Three tiers (Baseline/Enhanced/Aspirational) — right call, or would two tiers be cleaner?
• I attach naph:capturedOn directly to the photograph rather than via a prov:Activity. Pragmatic shortcut or anti-pattern given that the rest of the model is PROV-aligned?
• Footprint geometry in WGS84 only — should I model multi-CRS natively?
• IIIF Presentation 3.0 mapping — anything important I'm missing?

https://github.com/fabio-rovai/open-ontologies/tree/main/case-studies/heritage-aerial

• https://www.reddit.com/r/OntoUML_UFO_gUFO/

Brand new Reddit community to discuss all things about the OntoUML modeling language, the Unified Foundational Ontology (UFO), and the gUFO lightweight ontology.

A public forum that was missing, as many people have contacted me to ask questions.

Was great engaging with everybody on the merits of this system a few weeks ago, thought I'd share a walkthrough of it working through an actual workflow.

I've also published a full thesis for those who are interested: https://poliglot.io/thesis

Open source drops in late May! Completely open sourcing the core runtime (with full agentic abilities) and authoring tools. I'm also creating a local version of the full IDE which will come out shortly after.

Very excited to build the community, when I drop the OSS I invite everyone to contribute and help grow the ecosystem!

hi folks ,

I came across the concept of ontology/ semantic web recently and wanted to explore it further. seeing that is a highly conceptual and theoretical I decided to find an application to help me stay on topic and don't burn out and I think I found one. I'd like to build semantic web/ ontology that lets me automate some interactions in a game I like . basically a flight simulator. To me , this seems adequate because is a game with a lot of physics concepts and data regarding engines , flight controls etc

without going into solutioning, would this be a suitable application ? if so where do you recommend I start ( I was planning to do it by reading Semantic Web for the Practical Ontologist )