labelt

Labelt is a domain-specific language designed for annotating datasets with semantic labels and metadata to support machine learning, data governance, and reproducible experiments. The language emphasizes portability, human readability, and machine parsability, promoting consistent labeling across projects and teams. Labelt aims to bridge simple tag systems and more formal data-description languages by providing a lightweight, schema-driven approach.

Origins of Labelt trace to open-source data-labeling communities in the 2020s, seeking a common format to replace ad hoc annotations. While there is no single official standard, several community-driven specifications and dialects have emerged, each proposing core constructs for labels, attributes, provenance, and validation rules. The project ecosystem tends to favor interoperability with existing data catalogs and ML pipelines.

Key features include a schema system for labels and attributes, support for hierarchical and multi-label structures, provenance and versioning metadata, and built-in validation. The language is designed to serialize to multiple backends, including JSON-like and YAML-like representations, and to integrate with data catalogs, experiment trackers, and model training pipelines.

An annotation in Labelt consists of an entry with an identifier, a primary label, and optional attributes. Example (plain text): id=img123 label=dog attributes.color=brown attributes.size=medium provenance.source=datasetA confidence=0.87. Datasets are composed of such entries and may be grouped into scenes or collections, enabling scalable labeling and auditing.

Applications include labeling for computer vision, natural language processing, and multimodal datasets, as well as data provenance auditing and experiment reproducibility. Limitations include fragmentation across dialects, limited formal standardization, and varying tool support between platforms.

See also: Data labeling, Data governance, Taxonomy, Metadata.