| _version_ | 1866902029428850688 |
|---|---|
| author | Scrocca, Mario Carenini, Alessio Carriero, Valentina Anita Celino, Irene |
| author_facet | Scrocca, Mario Carenini, Alessio Carriero, Valentina Anita Celino, Irene |
| contents | <p>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.</p> <p>Authors: Mario Scrocca, Alessio Carenini, Valentina Anita Carriero and Irene Celino<br><br>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. <br>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.</p> <p>Paper: <a href="https://ceur-ws.org/Vol-4093/Paper4hai.pdf">https://ceur-ws.org/Vol-4093/Paper4hai.pdf</a> </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17702230 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | rapid-triples: Adaptive Forms for Semi-automatic Knowledge Collection in RDF Scrocca, Mario Carenini, Alessio Carriero, Valentina Anita Celino, Irene knowledge collection knowledge extraction knowledge graphs form interface human-in-the-loop <p>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.</p> <p>Authors: Mario Scrocca, Alessio Carenini, Valentina Anita Carriero and Irene Celino<br><br>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. <br>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.</p> <p>Paper: <a href="https://ceur-ws.org/Vol-4093/Paper4hai.pdf">https://ceur-ws.org/Vol-4093/Paper4hai.pdf</a> </p> |
| title | rapid-triples: Adaptive Forms for Semi-automatic Knowledge Collection in RDF |
| topic | knowledge collection knowledge extraction knowledge graphs form interface human-in-the-loop |
| url | https://doi.org/10.5281/zenodo.17702230 |