Foundations for a Large Graph Model

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.