Demo Data from SIGIR 2026 / reviewers in the wild / expert
Riccardo Volonterio
dblp:07/9795
· DBLP profile ↗
5ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 4Software engineering, systems software and programming languages · 3Applied, interdisciplinary, general and emerging computing · 1
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.
| Databases, data mining, and information retrieval
1 paper |
Knowledge graphs · 61% Web and social media mining · 30% Data integration and cleaning · 9% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge graphs › knowledge base population
emerging entity discovery |
0.3 | 1 | 2017 | Extracting Emerging Knowledge from Social Media · WWW 2017 |
Knowledge graphs
knowledge graph construction |
0.3 | 1 | 2017 | Extracting Emerging Knowledge from Social Media · WWW 2017 |
Web and social media mining
social media analysis |
0.3 | 1 | 2017 | Extracting Emerging Knowledge from Social Media · WWW 2017 |
Methods — techniques the papers use, named apart from their topics
ontology learning · 0.3entity extraction · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Extracting Emerging Knowledge from Social MediaabstractMassive data integration technologies have been recently used to produce very large ontologies. However, knowledge in the world continuously evolves, and ontologies are largely incomplete for what concerns low-frequency data, belonging to the so-called long tail. Socially produced content is an excellent source for discovering emerging knowledge: it is huge, and immediately reflects the relevant changes which hide emerging entities. Thus, we propose a method for discovering emerging entities by extracting them from social content. Marco Brambilla 0001, Stefano Ceri, Emanuele Della Valle, Riccardo Volonterio, Felix Xavier Acero Salazar |
WWW | 4 |
| 2016 | Modeling and Analyzing Engagement in Social Network Challenges
Marco Brambilla 0001, Stefano Ceri, Chiara Leonardi, Andrea Mauri 0001, Riccardo Volonterio |
WISE (1) | 5 |
| 2015 | Designing Complex Crowdsourcing Applications Covering Multiple Platforms and Tasks
Alessandro Bozzon, Marco Brambilla 0001, Stefano Ceri, Andrea Mauri 0001, Riccardo Volonterio |
J. Web Eng. | 5 |
| 2014 | Pattern-Based Specification of Crowdsourcing Applications
Alessandro Bozzon, Marco Brambilla 0001, Stefano Ceri, Andrea Mauri 0001, Riccardo Volonterio |
ICWE | 5 |
| 2011 | Exploratory Multi-domain Search on Web Data Sources with Liquid Queries
Davide Francesco Barbieri, Alessandro Bozzon, Marco Brambilla 0001, Stefano Ceri, Chiara Pasini, Luca Tettamanti, Salvatore Vadacca, Riccardo Volonterio, Srdan Zagorac |
ICWE | 8 |