---
title: "DAVE: A Framework for Assisted Analysis of Document Collections in Knowledge-Intensive Domains"
authors:
  - "Ruben Agazzi"
  - "Renzo Alva Principe"
  - "Riccardo Pozzi"
  - "Marco Ripamonti"
  - "Matteo Palmonari"
doi: "10.24963/ijcai.2025/1246"
url: "https://ijcai.org/proceedings/2025/1246"
pdf: "https://ijcai.org/proceedings/2025/1246.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "10984-10988"
track: "Demo Track"
keywords:
  - "Search: S: Applications"
  - "Natural Language Processing: NLP: Named entities"
  - "Natural Language Processing: NLP: Question answering"
  - "Humans and AI: HAI: Human-AI collaboration"
---

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

Ruben Agazzi, Renzo Alva Principe, Riccardo Pozzi, Marco Ripamonti, Matteo Palmonari

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 10984-10988. DOI: [10.24963/ijcai.2025/1246](https://doi.org/10.24963/ijcai.2025/1246)

## 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.