Kate Revoredo

53 papers receiving 242 citations

Peers

Kate Revoredo
Comparison fields: 5 of 56
  • Artificial Intelligence 118
  • Management Information Systems 98
  • Information Systems 95
  • Management Science and Operations Research 36
  • Molecular Biology 28
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Srividya Bansal United States
Emanuele Storti Italy
Mieczysław L. Owoc Poland
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Dina Nawara Canada
Abdel‐Rahman H. Tawil United Kingdom
Juan M. Alberola Spain
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Citations per field
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Citations per year

Countries citing papers authored by Kate Revoredo

Since Specialization
Citations

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

Fields of papers citing papers by Kate Revoredo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kate Revoredo

This figure shows the co-authorship network connecting the top 25 collaborators of Kate Revoredo. A scholar is included among the top collaborators of Kate Revoredo 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 Kate Revoredo. Kate Revoredo 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
#WorkIndexed citations
1 0
2 6
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5 7
6
Using word semantics on entity names for correspondence set generation.
2
7 5
8
ALIN Results for OAEI 2019.
4
9
Probabilistic relational reasoning in semantic robot navigation
1
10 0
11
On the Identification and Representation of Ontology Correspondence Antipatterns.
5
12 3
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Assertion Role in a Hybrid Link Prediction Approach through Probabilistic Ontology.
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14 2
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Ontology alignment based on instances using hybrid genetic algorithm
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An experimental evaluation of a scalable probabilistic description logic approach for semantic link prediction
1
17
Computing inferences for credal ALC terminologies
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18
Semantic link prediction through probabilistic description logics
4
19
Learning sentences and assessments in probabilistic description logics
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Semantic query extension through probabilistic description logics
1

About Kate Revoredo

Kate Revoredo is a scholar working on Management Information Systems, Information Systems and Artificial Intelligence, having authored 62 papers that have together received 257 indexed citations. Recurring topics across this work include Semantic Web and Ontologies (30 papers), Business Process Modeling and Analysis (21 papers) and Service-Oriented Architecture and Web Services (20 papers). The work is most often cited by research in Management Information Systems (98 citations), Information Systems (95 citations) and Artificial Intelligence (118 citations). Kate Revoredo has collaborated with scholars based in Brazil, Austria and Germany. Frequent co-authors include Flávia Maria Santoro, Fernanda Baião, Fábio Gagliardi Cozman, Leonardo Guerreiro Azevedo, Jan Mendling, Márcio de Oliveira Barros, Henrik Leopold, Paulo Mattos, Angelika Kimmig and Luc De Raedt. Their work appears in journals such as IEEE Access, IEEE Transactions on Intelligent Transportation Systems and Machine Learning.

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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