Re: Foundations for a Large Graph Model

Sorry, Context hierarchy is:

Context
Context::getObjects / getAttributes

Object extends Context
Object::getContexts / getAttributes

Attribute extends Context

Note:

This, my posts into this lists and the documents and "specifications"
published in the blog are not yet implemented thoughts posted in the hope
of someone could benefit realizing or implementing them and become a
stakeholder which will allow me to guide my theoretical and implementation
efforts.

Regards,
Sebastián.


On Mon, Mar 9, 2026, 9:28 PM Sebastian Samaruga <ssamarug@gmail.com> wrote:

> Daniel,
>
> Thanks a lot for your time and comments.
>
> Let me add some comments inline with your reply.
>
> On Mon, Mar 9, 2026, 11:58 AM Daniel Ramos <capitain_jack@yahoo.com>
> wrote:
>
>> Hi Sebastián,
>>
>> Thank you for sharing your FCA-based integration framework proposal on
>> the AIKR list. The idea of using Formal Concept Analysis contexts with SPO
>> triples for aggregation/alignment is an interesting formalization approach.
>>
>> A few observations that may help you refine the proposal:
>>
>> The encoding layer (prime embeddings + tensors + Apache Spark) would
>> benefit from concrete benchmarks showing how it compares to existing
>> knowledge graph embedding methods (TransE, RotatE, etc.)
>>
>> The reactive context-update model (Aggregation → Alignment → Activation)
>> is reminiscent of stream reasoning architectures — connecting to that
>> literature could strengthen the framing
>>
>
> Regarding streams, the proposal observes a functional reactive oriented
> approach based in the Occurrence Monad:
>
> (Context, Object, Attribute) : FCA Triples encoding.
>
> Occurrence Monad (Context hierarchy Contexts, Objects and Attributes
> wrapper): FCA Objects / Attributes in FCA Context w./ FCS Objects /
> Attributes.
>
> Context hierarchy bound functions:
>
> Context::getObjects / getAttributes
>
> Object::getContexts / getAttributes : Context
>
> Attribute::getContexts / getObjects : Context
>
> Consider publishing a small reproducible example demonstrating one
>> end-to-end pipeline (e.g., SPO ingestion → FCA lattice → prediction) to
>> make the proposal more concrete
>>
>> Best regards,
>> Daniel
>> On 3/9/26 11:49 AM, Sebastian Samaruga wrote:
>>
>> Foundations for a Large Graph Model:
>>
>> FCA (Formal Concept Analysis):
>> Contexts: Objects x Attributes matrix.
>>
>> SPO Input Triples Contexts:
>> SPOContextAxis. S / P / O Objects / Attributes.
>> Example Contexts:
>> P SPOContextAxis Predicates, S Objects, O Attributes.
>> S SPOContextAxis Subjects, P Objects, O Attributes.
>> O SPOContextAxis Objects, P Objects, S Attributes.
>>
>> (SPOContextAxis(O x A)) : O / A (Recursive labeled occurrences).
>>
>> Aggregation:
>> (StateContext(O : AggregatedTypeCtxAxis, A : SPOContextAxis))
>> (Working(Employee, worksAt))
>>
>> Alignment:
>> Attribute / Link prediction.
>> Type (upper / hiers / order) alignment.
>> (encoding)
>>
>> Activation:
>> Transforms: available actors in role in interaction context state changes
>> predictions.
>> (CurrentStateContext(PreviousStateContext x NextStateContext))
>> (Semisenior(Junior x Senior))
>> (encoding)
>>
>> Encoding:
>> FCA Contexts.
>> SPO / Kinds Occurrences.
>> Prime embeddings.
>> Tensors. Train (source context encodings) / Predict (materialize
>> contexts) reactive on contexts updates. Aggregation (classification),
>> Alignment (regression), Activation (clustering) Models. Apache Spark.
>>
>> References: https://sebxama.blogspot.com
>>
>> Best regards,
>> Sebastián.
>>
>>