Misael Mongiovı̀

1.1k total citations
43 papers, 687 citations indexed

About

Misael Mongiovı̀ is a scholar working on Artificial Intelligence, Computer Networks and Communications and Computer Vision and Pattern Recognition. According to data from OpenAlex, Misael Mongiovı̀ has authored 43 papers receiving a total of 687 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Artificial Intelligence, 11 papers in Computer Networks and Communications and 9 papers in Computer Vision and Pattern Recognition. Recurrent topics in Misael Mongiovı̀'s work include Topic Modeling (12 papers), Natural Language Processing Techniques (8 papers) and Semantic Web and Ontologies (8 papers). Misael Mongiovı̀ is often cited by papers focused on Topic Modeling (12 papers), Natural Language Processing Techniques (8 papers) and Semantic Web and Ontologies (8 papers). Misael Mongiovı̀ collaborates with scholars based in Italy, United States and France. Misael Mongiovı̀'s co-authors include Ambuj K. Singh, Petko Bogdanov, Valentina Presutti, Diego Reforgiato Recupero, Aldo Gangemi, Andrea Giovanni Nuzzolese, Alfredo Ferro, Rosalba Giugno, Alfredo Pulvirenti and Sergio Consoli and has published in prestigious journals such as Scientific Reports, Sensors and BMC Bioinformatics.

In The Last Decade

Misael Mongiovı̀

39 papers receiving 660 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Misael Mongiovı̀ Italy 15 339 165 149 119 118 43 687
Sujith Ravi United States 15 498 1.5× 159 1.0× 104 0.7× 56 0.5× 177 1.5× 24 809
Francesco Ricca Italy 17 775 2.3× 154 0.9× 181 1.2× 26 0.2× 101 0.9× 78 1.0k
Mohamed Aly United States 13 659 1.9× 185 1.1× 269 1.8× 90 0.8× 345 2.9× 28 1.1k
László Kovács Hungary 10 288 0.8× 98 0.6× 72 0.5× 30 0.3× 219 1.9× 120 654
Yanhao Wang China 11 310 0.9× 190 1.2× 98 0.7× 424 3.6× 148 1.3× 70 850
Teng-Sheng Moh United States 16 396 1.2× 350 2.1× 132 0.9× 48 0.4× 302 2.6× 95 905
William M. Pottenger United States 15 456 1.3× 91 0.6× 144 1.0× 42 0.4× 200 1.7× 57 717
Enrique Amigó Spain 16 840 2.5× 68 0.4× 156 1.0× 125 1.1× 388 3.3× 51 1.1k
Yuan Zuo China 13 603 1.8× 77 0.5× 68 0.5× 161 1.4× 211 1.8× 42 776
Yushi Jing United States 12 391 1.2× 89 0.5× 755 5.1× 67 0.6× 289 2.4× 16 1.1k

Countries citing papers authored by Misael Mongiovı̀

Since Specialization
Citations

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

Fields of papers citing papers by Misael Mongiovı̀

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Misael Mongiovı̀

This figure shows the co-authorship network connecting the top 25 collaborators of Misael Mongiovı̀. A scholar is included among the top collaborators of Misael Mongiovı̀ 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 Misael Mongiovı̀. Misael Mongiovı̀ is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Longo, Fabio, et al.. (2025). Eliciting metaknowledge in Large Language Models. Cognitive Systems Research. 91. 101352–101352. 1 indexed citations
2.
Mongiovı̀, Misael, et al.. (2025). Large Language Models meet moral values: A comprehensive assessment of moral abilities. Computers in Human Behavior Reports. 17. 100609–100609. 2 indexed citations
3.
Poggi, Francesco, et al.. (2025). Anatomy of climate change research in Italian doctoral dissertations using a machine learning approach. Scientific Reports. 15(1). 38095–38095.
4.
Longo, Fabio, et al.. (2025). Effective Hierarchical Text Classification with Large Language Models. SN Computer Science. 6(7).
5.
Mongiovı̀, Misael, et al.. (2024). HTC-GEN: A Generative LLM-Based Approach to Handle Data Scarcity in Hierarchical Text Classification. 129–138. 2 indexed citations
6.
Mongiovı̀, Misael, et al.. (2024). EX-CODE: A Robust and Explainable Model to Detect AI-Generated Code. Information. 15(12). 819–819. 2 indexed citations
7.
Mongiovı̀, Misael & Aldo Gangemi. (2024). GRAAL: Graph-Based Retrieval for Collecting Related Passages across Multiple Documents. Information. 15(6). 318–318.
8.
Becattini, Federico, et al.. (2023). VISCOUNTH: A Large-scale Multilingual Visual Question Answering Dataset for Cultural Heritage. ACM Transactions on Multimedia Computing Communications and Applications. 19(6). 1–20. 8 indexed citations
9.
Gangemi, Aldo, et al.. (2023). Towards Distribution-shift Robust Text Classification of Emotional Content. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna).
10.
Golinelli, Davide, Andrea Giovanni Nuzzolese, Francesco Sanmarchi, et al.. (2022). Semi-Automatic Systematic Literature Reviews and Information Extraction of COVID-19 Scientific Evidence: Description and Preliminary Results of the COKE Project. Information. 13(3). 117–117. 8 indexed citations
11.
Gangemi, Aldo, et al.. (2022). Uncovering Values: Detecting Latent Moral Content from Natural Language with Explainable and Non-Trained Methods. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 33–41. 11 indexed citations
12.
Mongiovı̀, Misael, et al.. (2020). REDUNET: reducing test suites by integrating set cover and network-based optimization. Applied Network Science. 5(1). 2 indexed citations
13.
Russo, Alessandro, Grazia D’Onofrio, Aldo Gangemi, et al.. (2018). Dialogue Systems and Conversational Agents for Patients with Dementia: The Human–Robot Interaction. Rejuvenation Research. 22(2). 109–120. 32 indexed citations
14.
Mongiovı̀, Misael, Diego Reforgiato Recupero, Aldo Gangemi, Valentina Presutti, & Sergio Consoli. (2016). Merging open knowledge extracted from text with MERGILO. Knowledge-Based Systems. 108. 155–167. 4 indexed citations
15.
Recupero, Diego Reforgiato, Andrea Giovanni Nuzzolese, Sergio Consoli, et al.. (2015). Extracting knowledge from text using SHELDON, a Semantic Holistic framEwork for LinkeD ONtology data. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 235–238. 9 indexed citations
16.
Mongiovı̀, Misael, et al.. (2015). Combining static and dynamic data flow analysis. 1573–1579. 10 indexed citations
17.
Mongiovı̀, Misael & Roded Sharan. (2012). Global Alignment of Protein–Protein Interaction Networks. Methods in molecular biology. 939. 21–34. 7 indexed citations
18.
Mongiovı̀, Misael, Ambuj K. Singh, Xifeng Yan, Bo Zong, & Konstantinos Psounis. (2012). Efficient multicasting for delay tolerant networks using graph indexing. 1386–1394. 39 indexed citations
19.
Mongiovı̀, Misael, et al.. (2009). A set-cover-based approach for inexact graph matching. 81–90. 3 indexed citations
20.
Ferro, Alfredo, et al.. (2008). GraphFind: enhancing graph searching by low support data mining techniques. BMC Bioinformatics. 9(S4). S10–S10. 15 indexed citations

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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