logokat

Logokat is a term used in speculative discussions in information science to describe a theoretical data construct that combines event logging with ontological categorization. The term is not part of a formal standard and has no single accepted definition, but is used to illustrate a design where logging metadata and semantic knowledge are tightly integrated.

In a logokat architecture, data consists of three layers: an event log that records time-stamped occurrences; a provenance layer that captures how data were produced and transformed; and an ontology layer that provides semantic categories and relationships. These layers are linked so that a query can retrieve not only the event but also its meaning, lineage, and related concepts.

Status: The concept emerged in academic and industry discussions about combined logging and knowledge representation in the mid-2020s. It has not been standardized and there is limited real-world adoption. Critics point to added complexity and performance costs, while proponents cite improved traceability, explainability, and cross-domain interoperability.

Applications include system auditing and compliance, scientific data management, digital forensics, and AI model explainability where traceability and semantic context are essential.

See also: data provenance, event logging, knowledge graphs, ontologies.