rapid-triples: Adaptive Forms for Semi-automatic Knowledge Collection in RDF

Slides for the presentation of the paper "rapid-triples: Adaptive Forms for Semi-automatic Knowledge Collection in RDF" accepted for publication at the 1st Workshop on Bridging Hybrid (Artificial) Intelligence and the Semantic Web (HAIBridge 2025) co-located with ISWC 2025.

Authors: Mario Scrocca, Alessio Carenini, Valentina Anita Carriero and Irene Celino

Abstract: To reduce inaccuracies or a lack of context, AI applications may heavily benefit from structured knowledge modelled relying on reference ontologies. We present rapid-triples, a customizable and dynamic interface that facilitates human-in-the-loop collection of structured knowledge in RDF format. The system allows domain experts to manually input knowledge or semi-automatically enhance an extraction by an automated system from existing data sources. 
By utilizing a common schema mapped to a target ontology, rapid-triples ensures that the knowledge collected is semantically interoperable and machine-readable. This tool supports various use cases, including expert-guided knowledge creation, validating and refining outputs from automated extractors, and generating high-quality training data for AI systems. Finally, we discuss the adoption of rapid-triples to support a use case in the industrial domain through the collection and exploitation of procedures as knowledge graphs.

Paper: https://ceur-ws.org/Vol-4093/Paper4hai.pdf