Classificationit
Classificationit is a theoretical framework for systematic classification of items into categories by integrating ontology-based reasoning with data-driven scoring. It posits a formal ontology of classes, relations, and constraints, along with item descriptions composed of attributes and contextual signals. A Classificationit classifier combines rule-based inference over the ontology with feature-driven scores from machine learning models and a calibration layer to produce class probabilities and interpretable justifications.
In practice, classification proceeds in stages: extraction of features and context; ontological reasoning to propagate and
Applications cited for Classificationit span digital asset management, document tagging, e-commerce product categorization, and biomedical taxonomies,
Strengths include transparency, adaptability to changing knowledge, and compatibility with active learning. Limitations include the need
Classificationit remains a conceptual framework described in theoretical discussions rather than a single standardized system. It