AI-Driven Knowledge Externalisation: From Unstructured Documents to Structured Data Models

Abstract

The transformation of unstructured textual information into structured, database-ready knowledge represents a critical challenge in contemporary information systems. Organisations accumulate vast amounts of documentation-technical specifications, project reports, procedural guidelines – yet much of this knowledge remains inaccessible to computational processing because it is non-structured. This paper proposes and analyses a method for artificial intelligence (AI)-driven knowledge mapping, whereby generative models are used to extract, interpret, and populate predefined data schemas from textual documents. The approach involves three key stages: (1) ingestion of a source document; (2) prompt-based semantic extraction aligned with a target database schema; and (3) structured object generation ready for persistence in relational or NoSQL environments. Unlike traditional information extraction techniques that rely on rigid rule-based systems or supervised training pipelines, the proposed method leverages prompt-engineered large language models (LLMs) to interpret context and map semantic content to structured attributes dynamically. This enables automated form completion, metadata generation, and structured representation of domain knowledge. The paper critically examines the reliability, consistency, and traceability of AI-generated mappings. While generative AI significantly reduces manual effort and accelerates data formalisation, challenges remain in ensuring validation, schema compliance, and epistemic transparency. The research, therefore, discusses validation mechanisms, confidence scoring, and human-in-the-loop verification strategies. By framing AI-driven extraction as a process of knowledge externalisation, the study positions this method within broader Knowledge Management (KM) theory. The implications extend beyond data automation: such mapping mechanisms enable scalable knowledge codification, improved interoperability between systems, and enhanced analytical capabilities. The findings suggest that AI-based structured extraction may redefine how organisations formalise expertise, shifting from document-centric storage toward schema-driven knowledge architectures.