Claudia d'Amato
Claudia d'Amato
Associate Professor, University of Bari
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Zitiert von
Zitiert von
Knowledge graphs
A Hogan, E Blomqvist, M Cochez, C d’Amato, GD Melo, C Gutierrez, ...
ACM Computing Surveys (Csur) 54 (4), 1-37, 2021
Anisa Rula, Lukas Schmelzeisen, Juan F. Sequeda, Steffen Staab, and Antoine Zimmermann
A Hogan, E Blomqvist, M Cochez, C d’Amato, G De Melo, C Gutierrez, ...
Knowledge graphs. CoRR, abs/2003.02320 10, 2020
DL-FOIL concept learning in description logics
N Fanizzi, C d’Amato, F Esposito
Inductive Logic Programming: 18th International Conference, ILP 2008 Prague …, 2008
Mining the Semantic Web: Statistical learning for next generation knowledge bases
A Rettinger, U Lösch, V Tresp, C d’Amato, N Fanizzi
Data Mining and Knowledge Discovery 24, 613-662, 2012
The data mining optimization ontology
CM Keet, A Ławrynowicz, C d’Amato, A Kalousis, P Nguyen, R Palma, ...
Journal of web semantics 32, 43-53, 2015
A semantic similarity measure for expressive description logics
C d'Amato, N Fanizzi, F Esposito
arXiv preprint arXiv:0911.5043, 2009
On the influence of description logics ontologies on conceptual similarity
C d’Amato, S Staab, N Fanizzi
International Conference on Knowledge Engineering and Knowledge Management …, 2008
Query answering and ontology population: An inductive approach
C d’Amato, N Fanizzi, F Esposito
European semantic web conference, 288-302, 2008
Tractable reasoning with Bayesian description logics
C d’Amato, N Fanizzi, T Lukasiewicz
Scalable Uncertainty Management: Second International Conference, SUM 2008 …, 2008
A dissimilarity measure for ALC concept descriptions
C d'Amato, N Fanizzi, F Esposito
Proceedings of the 2006 ACM symposium on Applied computing, 1695-1699, 2006
Conceptual clustering and its application to concept drift and novelty detection
N Fanizzi, C d’Amato, F Esposito
The Semantic Web: Research and Applications: 5th European Semantic Web …, 2008
Inductive learning for the semantic web: what does it buy?
C d'Amato, N Fanizzi, F Esposito
Semantic Web 1 (1-2), 53-59, 2010
Statistical learning for inductive query answering on OWL ontologies
N Fanizzi, C d’Amato, F Esposito
The Semantic Web-ISWC 2008: 7th International Semantic Web Conference, ISWC …, 2008
Ontology enrichment by discovering multi-relational association rules from ontological knowledge bases
C d'Amato, S Staab, AGB Tettamanzi, TD Minh, F Gandon
Proceedings of the 31st Annual ACM Symposium on Applied Computing, 333-338, 2016
Induction of concepts in web ontologies through terminological decision trees
N Fanizzi, C d’Amato, F Esposito
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2010
Classification of symbolic objects: A lazy learning approach
A Appice, C d'Amato, F Esposito, D Malerba
Intelligent Data Analysis 10 (4), 301-324, 2006
Machine learning for the semantic web: Lessons learnt and next research directions
C d’Amato
Semantic Web 11 (1), 195-203, 2020
Concept learning
J Lehmann, N Fanizzi, L Bühmann, C d’Amato
Perspectives on ontology learning 18, 71-91, 2014
Injecting background knowledge into embedding models for predictive tasks on knowledge graphs
C d’Amato, NF Quatraro, N Fanizzi
The Semantic Web: 18th International Conference, ESWC 2021, Virtual Event …, 2021
A declarative kernel for concept descriptions
N Fanizzi, C d’Amato
International Symposium on Methodologies for Intelligent Systems, 322-331, 2006
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