DAVE is a framework for assisting the analysis of documents in knowledge-intensive domains, based on an entity-centric approach supported by annotations of named entities in the documents. DAVE supports search & filtering, document exploration, question answering, and knowledge refinement. It is released as an open-source project that the community can further develop. DAVE’s distinguishing features are: the integration of a chatbot interface based on recent RAG solutions into well-established entity-powered faceted search, the fusion of search and filtering features provided by entity-level annotations with the capability to ask questions on annotated documents; human-in-the-loop functions to consolidate knowledge while exploring information, allowing users to improve annotations from NLP algorithms.

Agazzi, R., Alva Principe, R., Pozzi, R., Ripamonti, M., Palmonari, M. (2025). DAVE: A Framework for Assisted Analysis of Document Collections in Knowledge-Intensive Domains. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence Montreal, Canada 16-22 August 2025 (pp.10984-10988) [10.24963/ijcai.2025/1246].

DAVE: A Framework for Assisted Analysis of Document Collections in Knowledge-Intensive Domains

Alva Principe, Renzo;Pozzi, Riccardo
;
Ripamonti, Marco;Palmonari, Matteo
2025

Abstract

DAVE is a framework for assisting the analysis of documents in knowledge-intensive domains, based on an entity-centric approach supported by annotations of named entities in the documents. DAVE supports search & filtering, document exploration, question answering, and knowledge refinement. It is released as an open-source project that the community can further develop. DAVE’s distinguishing features are: the integration of a chatbot interface based on recent RAG solutions into well-established entity-powered faceted search, the fusion of search and filtering features provided by entity-level annotations with the capability to ask questions on annotated documents; human-in-the-loop functions to consolidate knowledge while exploring information, allowing users to improve annotations from NLP algorithms.
paper
Human-AI collaboration, Named entities, NLP, Question answering, Search Applications
English
Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25 - 16-22 August 2025
2025
Kwok, J
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence Montreal, Canada 16-22 August 2025
9781956792065
2025
10984
10988
https://www.ijcai.org/proceedings/2025/
reserved
Agazzi, R., Alva Principe, R., Pozzi, R., Ripamonti, M., Palmonari, M. (2025). DAVE: A Framework for Assisted Analysis of Document Collections in Knowledge-Intensive Domains. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence Montreal, Canada 16-22 August 2025 (pp.10984-10988) [10.24963/ijcai.2025/1246].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/572521
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