Pasquale Minervini

50 papers receiving 2.0k citations

Hit Papers

Convolutional 2D Knowledge Graph Embeddings2017202620202023201820174008001.2k

Peers

Pasquale Minervini
Comparison fields: 5 of 79
  • Artificial Intelligence 1.9k
  • Management Science and Operations Research 364
  • Computer Vision and Pattern Recognition 286
  • Statistical and Nonlinear Physics 224
  • Information Systems 213
Replace Shizhu He with:
Shizhu He China
Jamie Taylor
Colin Evans United States
Muhao Chen United States
Tim Dettmers Switzerland
Zaiqiao Meng United Kingdom
Parag Singla India
Shujian Huang China
Lejian Liao China
Berthold Reinwald United States
Pasquale Minervini relative to Shizhu He China Shizhu He's profile →
Citations per field
00.5×1.5×
Shizhu He · 1×
Citations per year

Countries citing papers authored by Pasquale Minervini

Since Specialization
Citations

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

Fields of papers citing papers by Pasquale Minervini

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pasquale Minervini

This figure shows the co-authorship network connecting the top 25 collaborators of Pasquale Minervini. A scholar is included among the top collaborators of Pasquale Minervini 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 Pasquale Minervini. Pasquale Minervini 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
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There is Strength in Numbers: Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training.
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Learning to propagate knowledge in web ontologies
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A graph regularization based approach to transductive class-membership prediction
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Learning Terminological Bayesian Classifiers - A Comparison of Alternative Approaches to Dealing with Unknown Concept-Memberships.
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Learning terminological naïve bayesian classifiers under different assumptions on missing knowledge
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About Pasquale Minervini

Pasquale Minervini is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Management Science and Operations Research, having authored 61 papers that have together received 2.1k indexed citations. Recurring topics across this work include Topic Modeling (25 papers), Natural Language Processing Techniques (14 papers) and Advanced Graph Neural Networks (14 papers). The work is most often cited by research in Artificial Intelligence (1.9k citations), Management Science and Operations Research (364 citations) and Computational Mathematics (15 citations). Pasquale Minervini has collaborated with scholars based in United Kingdom, Italy and Germany. Frequent co-authors include Sebastian Riedel, Pontus Stenetorp, Tim Dettmers, Tim Rocktäschel, Nicola Fanizzi, Claudia d’Amato, Edward Grefenstette, Leon Weber, Jannes Münchmeyer and Ulf Leser. Their work appears in journals such as Neurocomputing, Journal of Thoracic Oncology and Journal of Biomedical Informatics.

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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