clarifyres

Clarifyres is a framework and standard designed to clarify ambiguous resource identifiers and metadata within digital catalogs, knowledge bases, and information systems. It aims to improve search accuracy, data interoperability, and user trust by providing a consistent approach to disambiguation and metadata enrichment.

The Clarifyres project comprises a specification, reference implementations, and tooling for entity resolution and disambiguation. It relies on a combination of automated signals drawn from context, provenance, relationships between records, and user feedback. The framework is designed to be schema-agnostic and interoperable with common metadata models such as Dublin Core, BIBFRAME, and schema.org, and it supports integration through RESTful APIs and SPARQL endpoints.

Functionally, Clarifyres addresses cases where a single search query could map to multiple distinct resources—such as works with identical titles, author name variants, or multiple editions and formats. It generates disambiguation cues, assigns confidence scores, and can augment records with disambiguating metadata like author identifiers, edition dates, and publisher information. When automatic resolution is insufficient, it supports a human-in-the-loop workflow to confirm or correct matches, preserving audit trails for provenance.

Applications for Clarifyres span digital libraries, archives, museums, and institutional repositories, as well as research data management platforms. It is used to reduce record duplication, improve discovery, and enhance metadata quality across heterogeneous data sources. The concept emerged in the early 2020s through collaborative efforts across libraries and information-science communities and is maintained as an open framework to encourage widespread adoption and adaptation. Related topics include entity resolution, metadata normalization, and semantic interoperability.