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Ruben Agazzi

dblp:415/6330 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Language models and text generation · 62% Information extraction and text analysis · 19% Knowledge representation and reasoning · 19%
Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Language models and text generation
retrieval-augmented generation
0.912025
DAVE: A Framework for Assisted Analysis of Document Collections in Knowledge-Intensive Domains · IJCAI 2025
Information retrieval › document processing
document analysis
0.912025
DAVE: A Framework for Assisted Analysis of Document Collections in Knowledge-Intensive Domains · IJCAI 2025
Knowledge, reasoning and agents › Knowledge representation and reasoning › knowledge base
knowledge base refinement
0.312025
DAVE: A Framework for Assisted Analysis of Document Collections in Knowledge-Intensive Domains · IJCAI 2025
Natural language and speech › Information extraction and text analysis
named entity recognition
0.312025
DAVE: A Framework for Assisted Analysis of Document Collections in Knowledge-Intensive Domains · IJCAI 2025

Methods — techniques the papers use, named apart from their topics

retrieval-augmented generation · 1.7named entity recognition · 1.7faceted search · 1.7
YearPublicationVenuePosition
2025 DAVE: A Framework for Assisted Analysis of Document Collections in Knowledge-Intensive Domains
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.
Ruben Agazzi, Renzo Arturo Alva Principe, Riccardo Pozzi, Marco Ripamonti, Matteo Palmonari
IJCAI1