[netmod] Re: [NMOP] Re: Semantic meaning across representations and boundaries

[chong feng <[e-mail]>]

Hi Chris, Qin, all,

Thanks for the very insightful discussion. I think the discussion is
converging toward an important distinction: the semantic model itself and
the mechanisms that bind semantic models to existing representations are
related but different problems.

I agree with Chris that the fundamental challenge is to make meaning
explicit and independent from its representation form. In this sense,
ontology or semantic models provide a conceptual layer that describes
concepts, relationships, constraints, and contextual meaning. This layer is
not specific to YANG, REST API, or any particular data representation.

However, in practical infrastructure environments, we also need to consider
how such semantic meaning can interact with existing deterministic systems.
Most existing infrastructures will continue to use their current models and
interfaces, such as YANG-based management systems, REST APIs, CLI, and
other domain-specific models.

This is where I see the role of NAIM and RAIM. They are not intended to
replace ontology or define another universal knowledge model. Instead, they
provide semantic bindings between AI-consumable representations and
existing deterministic representations.

NAIM is an attempt to define such a semantic binding for YANG-based network
management systems, while RAIM (REST AI Interface Modeling) applies the
same idea to REST API based systems:

https://github.com/agentic-intent-network/raim-spec

A possible layered view could be:

              Ontology / Semantic Model
                       |
              Semantic Binding Layer
                  / \
              NAIM RAIM
                |           |
              YANG REST API

The upper layer defines shared meaning, while NAIM/RAIM address how that
meaning can be mapped into existing operational systems without requiring
changes to those systems.

It is also possible to operate without a fully defined ontology initially:

              LLM / Agent
                    |
              NAIM / RAIM
                    |
             Existing Systems

In this case, NAIM/RAIM provide an intermediate representation that is
easier for LLMs to consume than raw operational models, while preserving
compatibility with existing infrastructure.

Therefore, I think AI-consumable models have two complementary aspects:

defining machine/LLM-understandable meaning across domains;

providing practical mappings from that meaning to existing deterministic
systems.

The first is a semantic modeling problem, while the second is an
integration and interoperability problem. Keeping these two aspects
separate may help us better identify where different efforts (ontology,
knowledge graphs, annotations, NAIM, RAIM, etc.) fit.

Best,Frank

Christopher Janz <[e-mail]> 于 2026年8月5日周三 下午8:25写道：

> Qin — thank you for the careful review and detailed suggestions.
>
> On your side meeting's aim: understood, it's to frame the problem space
> for AI-consumable models across the OPS area, not only YANG, and to weigh
> the tools against the gaps. I am contributing to that discussion, but also
> pointing to related recent efforts to better grasp core problems.
>
> On terminology: I do not think I am introducing new terms. Form / meaning
> / use is Morris's original semiotic triad, and syntax / semantics /
> pragmatics are the standard names.
>
> I have taken two of your points: slide 5 now labels the bottom layer
> "syntax (form)", and since constraints are ontological I have added "axioms
> / constraints" to the ontology line on slides 5 and 6.
>
> But I'd push back on folding pragmatics into ontology. An ontology, per
> Gruber, is an explicit specification of a conceptualization: concepts,
> relations, their axioms or constraints, and the lexicon that names them.
> That is semantics. Pragmatics is a different axis: how concepts are used in
> context, who asserted concept or context and how surely, etc. The field
> keeps these apart deliberately, which is why provenance, named graphs and
> SKOS scope notes sit around an ontology rather than inside it, and it is
> where much of the operational value lives. The anomaly work illustrates it:
> action/reason/trigger is the ontology, while the lifecycle, the scores and
> the human-in-the-loop are pragmatics.
>
> So a framing that I think meets your instinct: keep "ontology" for the
> conceptual core, its concepts and relations and constraints and lexicon, as
> you prefer, and treat it as one component of the whole. The whole is the
> model's full meaning, its semantics and its pragmatics. I call that whole
> the "semantic model", using "semantic" in the broad semantic-web sense; I
> am not wedded to the name.
>
> On reference lexicon versus ontology vocabulary: it's true that lexicon
> and vocabulary are much the same word. The operative word is "reference". A
> reference lexicon is a vocabulary designated as an independent, shared
> anchor that systems bind to, kept deliberately thin: fixed identifiers,
> names, short definitions and examples, without the relations and axioms of
> a full ontology. That thinness is the point, since binding N systems to one
> shared reference scales where N-squared bilateral alignment does not. It
> supports reconciliation; it does not replace an ontology.
>
> On problem versus solution space: agreed, AI-consumable YANG is largely
> the problem framing and the others are approaches, and I am happy to state
> it that way. I take your smaller points on slides 4, 7, 8 and 11.
>
> On the IETF/IRTF boundary: whether and how NMOP and NMRG should
> collaborate is already a live question, as you know. Thomas has made a
> good specific suggestion in this exchange that points to a clean split:
> NMRG carries the ontology and reconciliation research, NMOP contributes the
> pragmatics and validation. I expect we will progress his proposal (probably
> narrowing to NMRG and NMOP lists) after the August vacation period.
>
> Updated slides are here:
> https://github.com/ChrisFJanz/meaning-across-representations/blob/main/semantic_model_deck.pdf
>
>
> Thanks again for the review and suggestions.
>
> Best
>
> Chris
>
> On Tue, Aug 4, 2026 at 11:43 PM Qin Wu <bill.wu=
> [e-mail]> wrote:
>
>> 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
>>
>>
>>
>>
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