categoriseen

Categoriseen is a theoretical framework for organizing knowledge that integrates categories with contextual attributes and relational links to form a dynamic, context-aware taxonomy. It treats categorization as an active process that adapts to use context, user goals, and temporal factors rather than a fixed, monolithic tree.

The term is used in contemporary information science and cognitive science discussions as a neologism for a contextualized, multi-axial approach to categorization. It is not a formal standard and has no single universal definition; rather, it appears in a variety of theoretical and application-focused writings to describe a class of methods rather than a single algorithm.

Core principles include context signals, multi-label assignments, and relational mapping across categories; temporal dynamics that allow categories to shift over time; and transparency so the reasoning behind category choices is traceable.

Implementation often combines graph-based structures, vector embeddings, and probabilistic scoring, with human-in-the-loop curation to resolve ambiguity.

Use cases include digital libraries and knowledge graphs, content classification in media platforms, e-commerce taxonomy management, and search systems requiring nuanced, context-sensitive results.

A hypothetical article about "mercury" could be categorized differently depending on whether the user reads it in a chemistry context or an astronomy context; categoriseen would assign multiple intersecting categories with weights corresponding to context.

Critics point to increased complexity, potential incompatibilities with existing taxonomies, and challenges in evaluation and interoperability.

Related concepts include categorization, ontology, taxonomy, knowledge graph, semantic web, and multi-label learning.