From Scattered to Structured: A Vision for Automating Architectural Knowledge Management

2026-01-27

AI Summary

This work addresses the challenge of architectural knowledge fragmentation across heterogeneous software artifacts, which often leads to architectural degradation due to inconsistent evolution. The paper presents the first end-to-end automated framework for architectural knowledge management, featuring specialized extractors that harvest knowledge from diverse sources, a unified representation model, and integrated mechanisms for consistency checking and repair to preserve knowledge integrity. Innovatively leveraging Retrieval-Augmented Generation (RAG), the framework enables compliance verification, change impact analysis, and natural language question answering grounded in a structured knowledge base. An experimental prototype demonstrates the approach’s effectiveness in enhancing architectural understanding, maintenance efficiency, and intelligent interaction.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Knowledge Representation and Reasoning: Knowledge Engineering
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Software architecture is inherently knowledge-centric. The architectural knowledge is distributed across heterogeneous software artifacts such as requirements documents, design diagrams, code, and documentation, making it difficult for developers to access and utilize this knowledge effectively. Moreover, as systems evolve, inconsistencies frequently emerge between these artifacts, leading to architectural erosion and impeding maintenance activities. We envision an automated pipeline that systematically extracts architectural knowledge from diverse artifacts, links them, identifies and resolves inconsistencies, and consolidates this knowledge into a structured knowledge base. This knowledge base enables critical activities such as architecture conformance checking and change impact analysis, while supporting natural language question-answering to improve access to architectural knowledge. To realize this vision, we plan to develop specialized extractors for different artifact types, design a unified knowledge representation schema, implement consistency checking mechanisms, and integrate retrieval-augmented generation techniques for conversational knowledge access.

Problem

• architectural knowledge
• knowledge management
• architectural erosion
• artifact inconsistency
• software maintenance

Innovation

• architectural knowledge management
• automated knowledge extraction
• structured knowledge base
• consistency checking
• retrieval-augmented generation