Mario Cataldi

858 citations
19 papers · 473 indexed · h-index 9

Impact in

Papers in

Mario Cataldi

18 papers receiving 440 citations

Peers

Mario Cataldi
Comparison fields: 5 of 64
  • Statistical and Nonlinear Physics 249
  • Information Systems 201
  • Communication 58
  • Artificial Intelligence 245
  • Transportation 24
Replace Thomas Gottron with:
Thomas Gottron Germany
Ovidiu Dan United States
Saša Petrović United Kingdom
Rolf Schwitter Australia
Carlos Martin United Kingdom
Keke Cai China
Armineh Nourbakhsh United States
Rongjing Xiang United States
Víctor Fresno Spain
Ajita John United States
Mario Cataldi relative to Thomas Gottron Germany Thomas Gottron's profile →
Citations per field
00.5×1.5×
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Citations per year

Countries citing papers authored by Mario Cataldi

Since Specialization
Citations

This map shows the geographic impact of Mario Cataldi'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 Mario Cataldi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mario Cataldi more than expected).

Fields of papers citing papers by Mario Cataldi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mario Cataldi. 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 Mario Cataldi. The network helps show where Mario Cataldi may publish in the future.

Co-authors

The 16 scholars most cited alongside Mario Cataldi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Mario Cataldi Line = papers co-authored together Mario Cataldi links everyone, so they are left out of the graph.

All Works

19 of 19 papers shown
#Work
1 2010321
2 201325
3 201324
4 201420
5 201417
6 200914
7 201412
8 201210
9 20188
10 20116
11 20155
12 20123
13
Structure- and Extension-Informed Taxonomy Alignment
20082
14 20102
15 20121
16 20111
17
Sentiment Analysis for Dynamic User Preference Inference in Spoken Dialogue Systems.
20141
18 20101
19
Context-informed Knowledge Extraction from Document Collections to Support User Navigation
20100

About Mario Cataldi

Mario Cataldi is a scholar working on Information Systems, Statistical and Nonlinear Physics, Artificial Intelligence, Management Science and Operations Research and Statistics, Probability and Uncertainty, having authored 19 papers that have together received 473 indexed citations. Recurring topics across this work include Semantic Web and Ontologies (7 papers), Recommender Systems and Techniques (5 papers), Advanced Text Analysis Techniques (4 papers), Complex Network Analysis Techniques (4 papers), Natural Language Processing Techniques (3 papers), Biomedical Text Mining and Ontologies (3 papers), Web Data Mining and Analysis (3 papers) and Optimization and Search Problems (2 papers). The work is most often cited by research in Statistical and Nonlinear Physics (249 citations), Information Systems (201 citations), Communication (58 citations), Artificial Intelligence (245 citations) and Transportation (24 citations). Mario Cataldi has collaborated with scholars based in Italy, France and United States. Frequent co-authors include Claudio Schifanella, Luigi Di, Marie-Aude Aufaure, Andrea Ballatore, K. Selçuk Candan, Emmanuel Viennet, Ilaria Tiddi, Maria Luisa Sapino, Sandro Rolesu and Stefano Cappai. Their work appears in journals such as Scientometrics, Small Ruminant Research, ACM Transactions on Intelligent Systems and Technology, Knowledge and Information Systems and Data & Knowledge Engineering.

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.

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