Demo Data from SIGIR 2026 / reviewers in the wild / expert
Marco Ripamonti
dblp:317/0196
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 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 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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Language models and text generation
retrieval-augmented generation |
0.9 | 1 | 2025 | DAVE: A Framework for Assisted Analysis of Document Collections in Knowledge-Intensive Domains · IJCAI 2025 |
Information retrieval › document processing
document analysis |
0.9 | 1 | 2025 | 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 |
0.3 | 1 | 2025 | 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 |
0.3 | 1 | 2025 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | DAVE: A Framework for Assisted Analysis of Document Collections in Knowledge-Intensive DomainsabstractDAVE 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 |
IJCAI | 4 |
| 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. | 6 |