schemacie

Schemacie is a theoretical framework for knowledge representation that organizes information into interconnected units called schemata. It emphasizes modularity, reusability, and context-sensitive reasoning, and is intended to support scalable data modeling and explainable AI.

The term schemacie draws on schema theory and denotes a field or process of organizing knowledge. It is a neologism used mainly in interdisciplinary writing; definitions vary by discipline, and there is no single standard formalism.

History: The concept emerged in the early 21st century and gained traction in discussions that seek to unify cognitive science with graph-based data models. Proponents describe schematic networks, or schema graphs, in which nodes represent schemata and edges denote hierarchical, relational, or causal connections.

Core concepts: A schemacie centers on schemata as modular units that can be composed, aligned, projected, and transformed. The framework uses a graph structure, enabling cross-domain reuse and traceability. It supports context mapping, where a single schema can be interpreted differently in different contexts.

Applications and examples: In data modeling and knowledge graphs, schemacie facilitates modular ontology design and reasoning. In education and AI, it supports adaptable curricula and explainable models. Example: a medical schemacie linking disease concepts, symptoms, tests, and treatments; a software schemacie mapping requirements to components.

Limitations and related concepts: Standardization is incomplete, and large schemacie networks can be complex to maintain. Related ideas include schemas, ontologies, knowledge graphs, and concept maps. See also: Schema, Ontology, Knowledge graph, Concept map, Schema theory.