Semantic Web / GenAI enabled EAI (Enterprise Application Integration) Framework Proposal
This post covers the inception phase documentation links related to a novel approach of doing EAI through the use of Functional / Reactive Programming leveraging GenAI and Semantic Web (graphs inference) and also the implementation of a novel approach of doing embeddings, not only for similarity calculation but also for relationships inference, query and traversal in an algebraic fashion.
Preface
Please let me shape a(nother) brief definition of intelligence:
The ability to convert entities Data (subjects key / value properties: product price) into Information (subjects key / value relationships, properties in a given context: product price across the last couple of months) and the ability to convert such Information into Actionable Knowledge (actionable tools / inferences into a given context / analogy: product price increase / decrease rate, determine if it is convenient to buy).
Introduction
In today's competitive landscape, organizations are often hampered by a portfolio of disconnected legacy and modern applications. This creates information silos, manual process inefficiencies, and significant barriers to innovation. This Application Integration Framework project is a strategic initiative designed to address these challenges head-on.
The project's core goal is to "integrate diverse existing / legacy applications or API services" by creating an intelligent middleware layer. This framework will automatically analyze data from various systems, understand the underlying business processes, and expose the combined functionality / use cases through a single, modern, and unified interface keeping in sync this interactions with the underlying integrated applications backends.
Goals
Implement a Semantic (graphs inference) / AI / GenAI enabled Business Intelligence / Enterprise Application Integration (EAI) platform with a reactive microservices backend leveraging functional programming techniques.
Implement a novel custom way to encode embeddings algebraically, enabling GenAI / MCP custom interactions, not just similarity but also mathematical relationships inference and reasoning. This by means of FCA (Formal Concept Analysis) contexts and lattices.
Expose an unified API façade / frontend (Generic Client / Hypermedia Application Language: HAL Implementation) of integrated applications use cases (Contexts) and use cases instances (Interactions) by means of Domain Driven Development and DCI (Data, Contexts and Interactions) design patterns and render inter-integrated applications use cases that could arise between integrated applications.
Approach
The idea is to build a layered set of semantic models, with their own levels of abstraction, backing a set of microservices from data ingestion from integrated business / legacy applications from their datasources, files and APIs feed to an Aggregation layer which performs type inference / matching, then to an Alignment layer which performs Upper Ontologies Matching and then to an Activation layer which exposes a unified interface to the integrated applications use cases, keeping in sync integrated applications backends with this Activation layer's interactions.
The proposal is not only to "integrate" but to "replicate" the functionalities of integrated or "legacy" applications based solely on the knowledge of their data sources (inputs and outputs) and, through heuristics (FCA: Formal Concept Analysis) and semantic inference, provide a unified API / frontend for each application's use cases (replicated) and for any use cases that may arise "between" integrated applications (workflows, wizards), all while keeping the original data sources synchronized.
Generic Client:
Incorporating or directly creating a new application or service (perhaps to be integrated with the previous ones) would simply be a matter of defining a source model schema and a set of initial reference data. And this today could certainly benefit greatly from GenAI / LLMs and MCP in both client and server modes.
Implementation
RDF / FCA (Formal Concept Analysis) for inference in an Aggregation layer, an FCA-based embeddings model for an Alignment layer and DDD (Domain Driven Development) / DOM (Dynamic Object Model) / DCI (Data, Context and Interaction) and Actor / Role Pattern for the above mentioned Activation layer.
References:
[FCA]:
[DDD]:
[DOM]:
[DCI]:
[Actor / Role]:
Specification drafts (long):
https://github.com/sebxama/sebxama/raw/refs/heads/main/ApplicationService.pdf
https://github.com/sebxama/sebxama/raw/refs/heads/main/RoadmapDetail1.4.pdf
Implementation Roadmap (Work In Progress, needs cleanup). Start reading RoadmapDetail3.1.pdf and RoadmapDetail3.6 (Algebraic Embeddings) to see if it is worth for you to continue reading:
https://github.com/sebxama/sebxama/raw/refs/heads/main/RoadmapDetail3.0.pdf
https://github.com/sebxama/sebxama/raw/refs/heads/main/RoadmapDetail3.1.pdf
https://github.com/sebxama/sebxama/raw/refs/heads/main/RoadmapDetail3.2.pdf
https://github.com/sebxama/sebxama/raw/refs/heads/main/RoadmapDetail3.3.pdf
https://github.com/sebxama/sebxama/raw/refs/heads/main/RoadmapDetail3.4.pdf
https://github.com/sebxama/sebxama/raw/refs/heads/main/RoadmapDetail3.5.pdf
https://github.com/sebxama/sebxama/raw/refs/heads/main/RoadmapDetail3.6.pdf
This draft documents covers just the starting of the initial specification and design phases. Advice is welcome in this initial phase about tools and implementation choices.
This is a "container" index document where I'll be putting conclusions from the previous blog post links contents:
https://github.com/sebxama/sebxama/raw/refs/heads/main/Index.docx
Regards, Sebastián Samaruga.
https://github.com/sebxama/sebxama
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