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Riccardo Pozzi

dblp:337/8067 · DBLP profile ↗
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4ranked-venue papers
2as first author
4since 2021 · last 2025
0000-0002-4954-3837ORCID · corroborated

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

Databases, data management, data science and information retrieval · 2 · 2 first-author · 2 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021Security and privacy · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 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
IJCAI3
2025 ReFactX: Scalable Reasoning with Reliable Facts via Constrained Generation
Riccardo Pozzi, Matteo Palmonari, Andrea Coletta, Luigi Bellomarini, Jens Lehmann 0001, Sahar Vahdati
ISWC (1)1
2024 Combining Knowledge Graphs and NLP to Analyze Instant Messaging Data in Criminal Investigations
Riccardo Pozzi, Valentina Barbera, Renzo Arturo Alva Principe, Davide Giardini, Riccardo Rubini, Matteo Palmonari
WISE (2)1
2024 An entity-centric approach to manage court judgments based on Natural Language Processing
Valerio Bellandi, Christian Bernasconi, Fausto Lodi, Matteo Palmonari, Riccardo Pozzi, Marco Ripamonti, Stefano Siccardi
Comput. Law Secur. Rev.5