Francesco Piccinno
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition
- Information Systems
- Management Science and Operations Research
- Communication
- Co-authors
- Paolo FerraginaYasemin AltünPeter ShawJulian Martin EisenschlosNigel CollierMandar JoshiPhilip MasseyFangyu Liu
- Topics
- Topic Modeling (11 papers)Natural Language Processing Techniques (11 papers)Data Quality and Management (2 papers)
- Cited by
- Artificial IntelligenceManagement Science and Operations ResearchComputer Vision and Pattern Recognition
- Journals
- Computational IntelligenceInfoscience (Ecole Polytechnique Fédérale de Lausanne)Cineca Institutional Research Information System (Tor Vergata University)
- Partner nations
- ItalyUnited StatesSwitzerland
In The Last Decade
Francesco Piccinno
15 papers receiving 220 citations
Peers
Comparison fields: 5 of 41
- Artificial Intelligence 200
- Computer Vision and Pattern Recognition 47
- Information Systems 39
- Management Science and Operations Research 30
- Communication 14
Countries citing papers authored by Francesco Piccinno
This map shows the geographic impact of Francesco Piccinno's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Francesco Piccinno with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Francesco Piccinno more than expected).
Fields of papers citing papers by Francesco Piccinno
This network shows the impact of papers produced by Francesco Piccinno. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Francesco Piccinno. The network helps show where Francesco Piccinno may publish in the future.
Co-authorship network of co-authors of Francesco Piccinno
This figure shows the co-authorship network connecting the top 25 collaborators of Francesco Piccinno. A scholar is included among the top collaborators of Francesco Piccinno based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Francesco Piccinno. Francesco Piccinno is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 4 | |
| 4 | 20 | |
| 5 | 23 | |
| 6 | 3 | |
| 7 | 10 | |
| 8 | 0 | |
| 9 | 12 | |
| 10 | 15 | |
| 11 | 7 | |
| 12 | 18 | |
| 13 | 23 | |
| 14 | Revisiting Taxonomy Induction over Wikipedia | 10 |
| 15 | 3 | |
| 16 | 89 |
About Francesco Piccinno
Francesco Piccinno is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing, having authored 16 papers that have together received 239 indexed citations. Recurring topics across this work include Topic Modeling (11 papers), Natural Language Processing Techniques (11 papers) and Data Quality and Management (2 papers). The work is most often cited by research in Artificial Intelligence (200 citations), Management Science and Operations Research (30 citations) and Computer Vision and Pattern Recognition (47 citations). Francesco Piccinno has collaborated with scholars based in Italy, United States and Switzerland. Frequent co-authors include Paolo Ferragina, Yasemin Altün, Peter Shaw, Julian Martin Eisenschlos, Nigel Collier, Mandar Joshi, Philip Massey, Fangyu Liu, Chenxi Pang and Kenton Lee. Their work appears in journals such as Computational Intelligence, Infoscience (Ecole Polytechnique Fédérale de Lausanne) and Cineca Institutional Research Information System (Tor Vergata University).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.