rapid-triples: Adaptive Forms for Semi-automatic Knowledge Collection in RDF
Year: 2025
Type: article
Abstract: 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 CelinoAbstract: 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
Authors: Scrocca, Mario, Carenini, Alessio, Carriero, Valentina Anita, Celino, Irene
Institution: Cefriel
Language: English
Topic: Semantic Web and Ontologies
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: European Commission
OpenAlex ID: W7106561512
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