Sensetagged

Sensetagged refers to a metadata practice in which digital items are annotated with sensory descriptors—tags that describe perceptual properties across modalities. The goal is to encode human-perceived sensory information alongside traditional metadata, enabling more nuanced search, accessibility, and immersive experiences across text, image, audio, and video content.

In a sensetagged data model, tags are organized into an ontology or taxonomy of sensory attributes. Common fields include visual attributes (color, brightness, saturation, contrast), and auditory attributes (volume, pitch, timbre). When relevant, tactile or olfactory descriptors may be used for virtual or augmented reality contexts. Tags can be simple keywords or structured as key-value pairs or RDF-like triples, often stored as JSON-LD or other linked data formats.

Applications include digital libraries and media repositories that support sensory search, e-commerce catalogs that convey product feel, accessibility tools that provide descriptive cues for users with impaired vision, and VR/AR environments that adapt content to user preferences or sensory contexts.

Challenges include subjectivity in sensory perception, varying cultural or language interpretations, and inconsistencies among annotators. Standardization efforts are ongoing to define taxonomies and validation methods. Performance and storage overhead, privacy considerations, and potential bias in sensory tagging are further risks to be addressed.

Example: an image tagged with color: blue, saturation: high; lightness: mid; texture: rough. An audio clip tagged with tempo: moderate; timbre: warm. Such tags enable faceted search, multimodal recommendation, and descriptive captions that aid accessibility.