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Re: Semantic Reactive Microservices

From: Paola Di Maio <paola.dimaio@gmail.com>
Date: Tue, 13 Nov 2018 10:48:30 +0800
Message-ID: <CAMXe=SpWDJSjEpZQvx9r_L_zyfpKUVs+bFxJJ81j=3GmW1aL1Q@mail.gmail.com>
To: ssamarug@gmail.com
Cc: SW-forum <semantic-web@w3.org>, public-lod@w3.org, public-rww@w3.org, public-aikr@w3.org
Dear Sebastian
thanks for the note -
is this a sketch of some kind of microservice architecture and
what do you think we should do with it?
consider writing a paper?
I think the work we have to do at AI KR  CG is to establish  the spectrum
for our scope of work in relation to what we want to focus on
since AIKR is very broad, then reaise the questions against what we
consider in scope
pls let us know
P






On Tue, Oct 16, 2018 at 3:48 AM Sebastian Samaruga <ssamarug@gmail.com>
wrote:

> Draft: could a framework of patterns leverage SW / ML adoption for end to
> end business applications / BI use cases: (sending this again because
> download link in blog is not working)
>
> Semantic Web / RDF as the 'glue' of / for ML dataflow encoded input
> 'features' / output 'tensors' and ontology aligments. Mappings for
> knowledge input / augmented (learning) output rows (RDBMS example)
> processed by ML models in semantic alignments.
>
> SW contexts encodes 'meaning' into translated input features / obtained
> learning output tensors via RDF CSPO quads Resource ID creation /
> assignment algorithm. Tensor shapes rendered as algorithmically Resource ID
> enabled (ANNs activation functions) operating over and preserving Resource
> IDs / Statements integrity (validation).
>
> Dataflow semantic 'forms' application language: encode code and data
> functionally as Context RDF quad statements. Context activations performs
> functional intra / inter Context transforms across layers statements.
> Encoded 'form' statement resolves to getters / setters applications by
> means of algorithm to obtain resulting inferred 'forms' (templates: system
> resource encoded quads).
>
> Tools:
>
> *TensorFlow / ML:*
>
> I/O: Tensors (features / classes, discrete values in 'shapes').
>
> Learning:
> Classification: class / instance identification. "Messi: Player : 10"
>
> Learning:
> Clustering: similarity (common attributes / links resolution). "Messi
> player of Barcelona".
>
> Learning:
> Regression (discrete value in function of input features, roles in
> contexts: value / event for x when y was z in w). "Messi captain of
> Barcelona in last tournament".
>
> Semantic Microservices (proposed component):
>
> I/O: RDF encoded features / outputs CSPO quad statements (reactive stream
> events bus). Resource ID creation / assignation algorithm (Semantic IDs:
> operable, tensor embeddings).
>
> Augmentation:
> Aggregations: data, schema, behavior statement layers dimensional
> aggregation. Type inference by attributes / values aggregation.
>
> Alignments:
> ID resolution: class / instance identity discovery (ontology / schema
> matching) ML models.
> Attributes / links resolution: clustering ML models
> Roles in contexts resolution: regression ML models
>
> *Distribution / Dataflow:*
>
> Integration / Discovery / Activations.
> Contexts / Layers: Dimensional upper ontology layers alignments between
> Contexts (data, domain, application levels).
>
> Reactive Extensions (RX). Dataflow 'forms' enabled 'templates' inter
> context levels.
>
> Activation: Resources Context's streams as observers / observables (RX) of
> Context / upper layers events. Event ('form') fires node augmentation
> (learning) / resolves to nodes emmiting knowledge 'forms' events related to
> their knowledge of the source event.
>
> *Use Cases:*
>
> Semantic Microservices Adapters (endpoints, integration / transforms).
>
> GraphQL: adapters schema / tenmplate transforms. Forms functional language
> translation (I/O: integration)..
>
> Adapters: Workflows / API Rendering (OData, REST: Spring HATEOAS / HAL).
>
> Refine / ETL (Adapters I/O).
>
> Big Data Deployments (Adapters I/O).
>
> BI / Dashboards (Adapters I/O).
>
> Declarative Business Applications Framework (Adapters I/O).
>
> *Links:*
>
> OData: https://www.odata.org
> Spring HATEOAS: https://spring.io/projects/spring-hateoas
> HAL: http://stateless.co/hal_specification.html
> http://openrefine.org/
> http://www.opencalais.com
> https://solid.mit.edu
>
> Sebastian Samaruga.
> http://exampledotorg.blogspot.com
>
Received on Tuesday, 13 November 2018 02:49:31 UTC

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