- Overview - Systems that organize information into coherent, navigable forms. - Provide scaffolding for understanding, memory, reasoning, and learning. - Useful across [[Cognitive Science]], [[AI]], [[education]], and [[Personal Knowledge Management (PKM)]]. - Purposes - Clarify relationships between ideas. - Support retrieval and long-term memory. - Enable reasoning through hierarchy, network, or schema structures. - Reduce cognitive load by imposing order on complexity. - Major Forms - [[Concept Tree]]s - Hierarchical parent-child organization. - Moves from abstract to specific. - Encodes edges such as “is-a” or “part-of”. - Supports traversal and inferencing. - [[Semantic Network]] - Graphs of interconnected concepts. - Rich, non-hierarchical relationships. - Models associative memory and flexible reasoning. - [[Ontology]] - Formal, machine-interpretable structures defining entities and relations. - Used in [[AI]], knowledge graphs, and computational semantics. - [[Schemas]] - Cognitive frameworks for interpreting experience. - Guide perception, inference, and expectation. - [[Taxonomy]] - Strict hierarchical classification systems. - Useful for grouping, labeling, and systematic categorization. - Key Components - Nodes - Core units of meaning or entities. - Edges - Connections describing semantic or structural relationships. - Hierarchy - Levels of abstraction. - Properties - Attributes, definitions, and metadata. - Traversal Mechanisms - Methods for navigating and querying the structure. - Related - [[Concept Tree]] - [[Ontology]] - [[Semantic Network]] - [[Schema Theory]]