Knowledge Model Architecture

Overview

This entry defines the internal structure of the Knowledge-Driven Analytical System. Before engineering reasoning can be formalized into checks, calculations, or decision support, the knowledge itself must be organized in a clear and reusable way.

Why a knowledge model

Engineering logic often depends on information that is understood implicitly but rarely stored explicitly. Parameters, limits, formulas, contextual conditions, and decision criteria must be separated into stable object types if they are to be reused consistently.

Object strategy

The system is intended to store engineering knowledge not only as text, but as structured objects that can later be linked, evaluated, and expanded.

Core object types

The first model is structured around a limited set of reusable knowledge objects that can later be linked, evaluated, and expanded.

Knowledge Object Map

A structural view of how reusable knowledge objects support later rule logic and analytical evaluation.

Input knowledge

Parameter

Defines a measurable or selectable engineering value.

Constraint

Defines a limit, requirement, or admissible boundary.

Context

Defines the technical situation in which knowledge applies.

Reference

Links knowledge to standards, sources, or external justification.

Logic core

Formula

Transforms parameters into calculable analytical relationships.

Rule

Applies explicit logic, checks, and decision conditions.

Component

Anchors the knowledge model to a technical object or system element.

Outputs

Decision Output

Summarizes the evaluated result, status, or recommendation.

Risk Flag

Highlights warnings, conflicts, or exceeded limits.

Relationship logic

These objects do not exist in isolation. Parameters belong to contexts, formulas depend on parameters, rules evaluate constraints, and decision outputs summarize analytical results.

Development status

This layer is the practical foundation of the wider system. The current priority is to define vocabulary, object boundaries, and structural relationships before moving toward richer analytical behavior.

Long-term outlook

Once the knowledge structure becomes stable, it can support later rule formalization, calculation modules, traceable evaluation workflows, and broader system growth.