Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Riccardo Volonterio

dblp:07/9795 · DBLP profile ↗
← Back
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

TopicWeightPapersLastEvidence papers
Knowledge graphs › knowledge base population
emerging entity discovery
0.312017
Extracting Emerging Knowledge from Social Media · WWW 2017
Knowledge graphs
knowledge graph construction
0.312017
Extracting Emerging Knowledge from Social Media · WWW 2017
Web and social media mining
social media analysis
0.312017
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
YearPublicationVenuePosition
2017 Extracting Emerging Knowledge from Social Media
abstract
Massive 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
WWW4
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
ICWE5
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
ICWE8