PROV-A (the Provenance App) is a web-based tool designed to streamline the creation and publication of provenance information as Linked Open Data (LOD). By integrating AI-driven natural language processing (NLP) with human validation, PROV-A balances efficiency with scholarly rigor, making provenance research more accessible and scalable for cultural institutions.
- Web-based, client-side application
- Supports integration of automated data extraction workflows
- Structured provenance data based on CIDOC-CRM standard
- User-friendly interface for data structuring
- Generates Linked Open Data structured as nanopublications
- SPARQL endpoint for querying generated LOD
PROV-A is available at: prov-a.github.io
- Preprocessed Data Schema: Users can format their provenance data according to a predefined JSON schema before uploading it into PROV-A. The schema is available at: JSON Schema
- Generated RDF SHACL: The structure of RDF output follows a data model available at: SHACL Schema
- Initialise Project: Enter metadata, select licensing, and input artefacts records.
- Structure Data: Validate and refine AI-extracted provenance information.
- Generate LOD: Transform provenance data into RDF and query the SPARQL endpoint.
A dedicated testing directory is available for validating data transformations and workflow consistency. See Testing Directory.
PROV-A leverages open-source libraries, including:
- N3.js: RDF quads writer
- Quadstore: LevelDB-backed RDF graph database
- quadstore-comunica: SPARQL qery engine
PROV-A is released under the MIT License. See LICENSE for details.
Created by Fabio Mariani as part of the Provenance Lab, funded through the Lichtenberg Professorship of Lynn Rother by the Volkswagen Foundation.