Development Of An AI-Ready Structured Knowledge Base For Classical Homeopathic Materia Medica: A Conceptual Database Architecture And Knowledge Representation Framework

The classical homeopathic materia medica   the works of Hahnemann, Kent, Boericke, Allen, and Clarke   encodes more than two centuries of clinically verified remedy knowledge, yet that knowledge remains locked in narrative prose that resists computational retrieval, cross-author comparison, and integration with modern artificial-intelligence tools. This short communication sets out the design of a structured, AI-ready knowledge base for the classical materia medica. We propose a conceptual framework rather than a validated software implementation. At its core is an entity–relationship data model that captures remedies, symptoms, modalities, mind and generalities, and inter-remedy relationships (complementary, inimical, and follows-well), with every record tagged to the source text from which it derives; we then weigh relational against graph database architectures for representing these data. After distinguishing the proposal from a conventional repertory, we outline an extraction pipeline that pairs OCR-cleaned digitized source texts with named-entity recognition and expert curation, and we describe three representative applications: structured symptom search, repertorization support, and retrieval-augmented generation (RAG) for grounding large language model responses in verified classical sources. Because the architecture is proposed rather than deployed, the work is offered as a template for digitizing traditional-medicine corpora while preserving fidelity to the primary texts.