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Alessandro Agostini

dblp:91/1045 · DBLP profile ↗
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7ranked-venue papers
5as first author
1since 2021 · last 2021
0000-0002-8877-315XORCID · corroborated

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

Artificial intelligence and machine learning · 4 · 4 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 3 · 3 first-authorDatabases, data management, data science and information retrieval · 2 · 2 first-author · 1 since 2021Software engineering, systems software and programming languages · 1Applied, 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.

Artificial intelligence
1 paper
Knowledge representation and reasoning · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning
automated reasoning
0.011997
Modularity in Computer Assisted Reasoning Systems · IJCAI 1997
Knowledge, reasoning and agents › Knowledge representation and reasoning
modularity
0.011997
Modularity in Computer Assisted Reasoning Systems · IJCAI 1997
YearPublicationVenuePosition
2021 UZWORDNET: A Lexical-Semantic Database for the Uzbek Language
abstract
The results reported in this paper aim to increase the presence of the Uzbek language in the Internet and its usability within IT applications.We describe the initial development of a "word-net" for the Uzbek language compatible to Princeton Word-Net.We called it UZWORDNET.In the current version, UZWORDNET contains 28140 synsets, 64389 sense and 20683 words; its estimated accuracy is 75.98%.To the best of our knowledge, it is the largest wordnet for Uzbek existing to date, and the second wordnet developed overall. *The acronym "LDKR" means Language, Data, Knowledge, and Reasoning.The LDKR Group aims to discovering (learning), modeling, reducing and computing the "semantic gap" between users and the Universe of Language(s), Data, Information and Knowledge their ICT systems are based on. 1 For instance, see https://medium.com/@robert.munro/bias- in-ai-3ea569f79d6a (accessed
Alessandro Agostini, Timur Usmanov, Ulugbek Khamdamov, Nilufar Abdurakhmonova, Mukhammadsaid Mamasaidov
GWC1
2008 Using Web Content Management Systems for Accessibility: The Experience of a Research Institute Portal
Laura Burzagli, Francesco Gabbanini, Marco Natalini, Enrico Palchetti, Alessandro Agostini
ICCHP5
2006 Coordination Through Inductive Meaning Negotiation
Alessandro Agostini
ECAI1
2004 On the Discovery of the Semantic Context of Queries by Game-Playing
Alessandro Agostini, Paolo Avesani
FQAS1
2003 A Peer-to-Peer Advertising Game
Paolo Avesani, Alessandro Agostini
ICSOC2
2000 Team-Solvability: A Model-Theoretic Perspective
Alessandro Agostini
ECAI1
1997 Modularity in Computer Assisted Reasoning Systems
Alessandro Agostini
IJCAI1