[netmod] Re: Semantic meaning across representations and boundaries

[Qin Wu <[e-mail]>]

Hi, Chris:
Thank for kicking off discussion on AI consumable model, the intention of AI Consumable Data Model Side Meeting is to articulate problem space of AI consumable model work in IETF OPS area, not limited to YANG and describe the key challenges and gaps, discuss how these challenges and gaps can be tackled or resolved with tool development, RDF/OWL based knowledge graph/ontology, YANG, or semantics enhancement, etc.
https://mailarchive.ietf.org/arch/msg/nmop/pKOnwWc7eFl5kKk7gWtD2tjCeGc/
If we limit the scope to AI only, ONSEN and NETMOD are not relevant, although we see many proposals in the past to discuss Guidelines for Translation of UML Information Model to YANG Data Model in the NETMOD.
I tend to agree there are more space in NMRG to tackle this kind of topics, especially ontology. But I think Ontology defines a machine-readable vocabulary, taxonomies, and conceptual relationships and acts as the overarching framework that synthesises Semantics, Constraints, and Context into a unified, machine-readable knowledge base. Therefore I have 2 concerns to this semantic model topic discussion:

1.       Such topic discussion makes boundary between IETF and IRTF become blurred.

2.       Introduce more new terms to describe what we have already had. What I would like suggest to stick to use ontology as a basis or umbrella
A few comments on attached slides with my biased humble opinions:
#Slide 2:
I think AI-consumable YANG or AI-consumable Data Model focuses on outlining the problem space, Ontology reconciliation, NAIM, knowledge graphs and annotation, multi-vendor normalisation, the ONSEN YANG↔TMF bridge are potential solutions to address these problems.
In addition, not clear how reference lexicon is different from Ontology vocabulary.

#Slide 4
When you say to create their semantic representations, it can be interpreted in different way:

1.       We can add more semantics in the form Data Model ,which can be consumed by AI such as semantic model defined in draft-ietf-nmop-network-anomaly-semantics

2.       You can create a totally brand new semantic representation, Intent ontology model can be a good example

#Slide 5

1．  Suggest to replace “form” with “syntax”, which is better to explain the relation with semantics

2．  I think ontology should comprise both PRAGMATICS and SEMANTICS, also suggest to replace PRAGMATICS with Context, Constraints, don’t need to introduce new terms to add confusion

3.       Call combination of PRAGMATICS and SEMANTICS as semantic model is a little bit narrow sense.

#Slide 6
1. Ontology should include Context information, Constraints information or PRAGMATICS
2. You can list RDF, OWL, YANG as examples for data model

# Slide 7
I think the commonality of all these cases exepct LIFT is to explore how to do intent translation using Ontology, knowledge Graph, Knowledge base, it is challenging to define common practice for intent translation.
We have many lessons in the past.

#Slide 8
I think NAIM and draft-prabhu-nmrg-prompt-schema-llm share lots of commonability.

#Slide 11
Interesting observation, I like it, I am wondering what lessons can we learnt from the past?

#Slide 13
I think AI Consumable YANG focus on problem space while the other proposals such as ontology reconciliation focus on solution space.

-Qin
发件人: Christopher Janz [mailto:christopher.janz=[e-mail]]
发送时间: 2026年7月31日 21:00
收件人: nmop <[e-mail]>; [e-mail]; [e-mail]; [e-mail]; [e-mail]
主题: [netmod] Semantic meaning across representations and boundaries

All – as I think has been broadly recognized during meetings last week and through various activities and discussion threads both leading up to and following them, a common theme and general problem seems to have emerged, in various ways, within and across NMOP, NETMOD, ONSEN, NMRG, and now as one element of the AIOps discussion.

These multiple discussions and problem contexts point at the same need: for semantic models for network information — meaning made explicit and independent of form – capable of supporting bridging of model and system boundaries and information consumption by machines.

I have put some form, structure and justification around a problem statement and tied it all to the various takes/contexts mooted and discussions in play. In the end I think there is a relatively focused but true research problem to be considered, which I have outlined and would suggest properly belongs in NMRG. For further discussion there … of course such a program would not preclude related activities and outcomes in other WGs, perhaps working against narrower/more specific targets.

Please have a look, and respond with your thoughts – if you wish, on the github repository here: https://github.com/ChrisFJanz/meaning-across-representations ... The deck is there, and also attached here for convenience.

Best

Chris