Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Knowledge Graphs
2021632 citationsAidan Hogan, Eva Blomqvist et al.ACM Computing Surveysprofile →
Citations per year, relative to Claudia d’Amato Claudia d’Amato (= 1×)
peers
Michael Cochez
Countries citing papers authored by Claudia d’Amato
Since
Specialization
Citations
This map shows the geographic impact of Claudia d’Amato'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 Claudia d’Amato with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Claudia d’Amato more than expected).
This network shows the impact of papers produced by Claudia d’Amato. 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 Claudia d’Amato. The network helps show where Claudia d’Amato may publish in the future.
Co-authorship network of co-authors of Claudia d’Amato
This figure shows the co-authorship network connecting the top 25 collaborators of Claudia d’Amato.
A scholar is included among the top collaborators of Claudia d’Amato 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 Claudia d’Amato. Claudia d’Amato is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Keet, C. Maria, Agnieszka Ławrynowicz, Claudia d’Amato, et al.. (2015). The Data Mining OPtimization Ontology. Journal of Web Semantics. 32. 43–53.58 indexed citations
7.
Minervini, Pasquale, Claudia d’Amato, Nicola Fanizzi, & Volker Tresp. (2014). Learning to propagate knowledge in web ontologies. CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro). 13–24.2 indexed citations
8.
d’Amato, Claudia, Nicola Fanizzi, Floriana Esposito, & Thomas Lukasiewicz. (2013). Representing Uncertain Concepts in Rough Description Logics via Contextual Indiscernibility Relations. Oxford University Research Archive (ORA) (University of Oxford).2 indexed citations
9.
Minervini, Pasquale, Claudia d’Amato, & Nicola Fanizzi. (2012). A graph regularization based approach to transductive class-membership prediction. 39–50.1 indexed citations
10.
d’Amato, Claudia, Volha Bryl, & Luciano Serafini. (2012). Data-driven logical reasoning. CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro). 900. 51–62.3 indexed citations
11.
Minervini, Pasquale, Claudia d’Amato, & Nicola Fanizzi. (2012). Learning Terminological Bayesian Classifiers - A Comparison of Alternative Approaches to Dealing with Unknown Concept-Memberships.. CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro). 191–205.2 indexed citations
12.
Minervini, Pasquale, Claudia d’Amato, & Nicola Fanizzi. (2011). Learning terminological naïve bayesian classifiers under different assumptions on missing knowledge. CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro). 63–74.1 indexed citations
13.
Fanizzi, Nicola, Claudia d’Amato, & Floriana Esposito. (2009). Evidential nearest-neighbors classification for inductive ABox reasoning. CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro). 27–38.1 indexed citations
d’Amato, Claudia, Nicola Fanizzi, & Floriana Esposito. (2008). A Note on the Evaluation of Inductive Concept Classification Procedures.. CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro).4 indexed citations
16.
Fanizzi, Nicola, Claudia d’Amato, & Floriana Esposito. (2007). Approximate measures of semantic dissimilarity under uncertainty. CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro). 61–72.1 indexed citations
17.
Fanizzi, Nicola, Claudia d’Amato, & Floriana Esposito. (2007). Induction of Optimal Semi-distances for Individuals based on Feature Sets.. CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro).10 indexed citations
18.
d’Amato, Claudia, Nicola Fanizzi, & Floriana Esposito. (2006). Reasoning by Analogy in Description Logics Through Instance-based Learning.. CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro).14 indexed citations
19.
d’Amato, Claudia, Nicola Fanizzi, & Floriana Esposito. (2005). A Semantic Dissimilarity Measure for Concept Descriptions in Ontological Knowledge Bases. CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro).2 indexed citations
20.
d’Amato, Claudia, Nicola Fanizzi, & Floriana Esposito. (2005). A Dissimilarity Measure for the ALC Description Logic.. CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro).2 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.