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README.md

Knowledge Objects

Knowledge Objects are the fundamental building blocks of BA Knowledge Assistant.

Instead of storing business knowledge as isolated documents, the system represents every important business concept as an independent reusable knowledge object.

This approach enables AI to retrieve precise business information, understand relationships between concepts, and generate consistent analytical artifacts.


Why Knowledge Objects

Traditional document repositories contain valuable information, but knowledge is usually buried inside large documents.

Business analysts repeatedly search through specifications, meeting notes, emails, and project documentation to reconstruct business context.

Knowledge Objects eliminate this problem by transforming business knowledge into reusable entities.

Each object becomes:

  • independently addressable;
  • reusable across projects;
  • semantically linked;
  • AI-readable;
  • traceable.

Typical Knowledge Objects

The LocalWiki knowledge model includes objects such as:

  • Business Goal
  • Business Need
  • Stakeholder
  • Business Process
  • Business Rule
  • Functional Requirement
  • Non-Functional Requirement
  • Constraint
  • Assumption
  • Risk
  • Decision
  • Acceptance Criteria
  • Test Case
  • Lesson Learned

Object Structure

Every knowledge object follows a standardized structure.

Typical metadata includes:

  • unique identifier;
  • title;
  • description;
  • relationships;
  • source reference;
  • lifecycle status;
  • tags;
  • version;
  • traceability links.

This standardization allows AI to retrieve knowledge consistently regardless of the original document format.


Relationships

Knowledge Objects are interconnected.

For example:

Business Goal

Business Need

Business Requirement

Functional Requirement

Acceptance Criteria

Test Case

This graph-based structure allows AI to reconstruct business context instead of relying on keyword search alone.


Business Value

Using Knowledge Objects enables organizations to:

  • preserve corporate documents;
  • reduce duplicated analytical work;
  • improve requirement consistency;
  • increase traceability;
  • accelerate onboarding;
  • support long-term knowledge reuse;
  • build AI-ready corporate documents bases.