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P. Mancarella, Raffaetà, A., Renso, C., and Turini, F., “Integrating knowledge representation and reasoning in Geographical Information Systems”, International Journal of Geographical Information Science, vol. 18, pp. 417-447, 2004.
P. Mancarella, Raffaetà, A., Renso, C., and Turini, F., “Integrating Knowledge Representation and Reasoning in Geographical”, 2004.
S. Mascetti, Ricci, A., and Ruggieri, S., “Introduction to special issue on computational methods for enforcing privacy and fairness in the knowledge society”, Artificial Intelligence and Law, vol. 22, pp. 109–111, 2014.
S. Ruggieri, “Introduction to the special issue on Artificial Intelligence for Society and Economy”, Intelligenza Artificiale, vol. 9, pp. 23–23, 2015.
L. Pappalardo, Grossi, V., and Pedreschi, D., “Introduction to the special issue on social mining and big data ecosystem for open, responsible data science”, 2021.
L. Pollacci, Guidotti, R., Rossetti, G., Giannotti, F., and Pedreschi, D., “The italian music superdiversity”, Multimedia Tools and Applications, pp. 1–23, 2018.
S. Rinzivillo and Turini, F., “Knowledge discovery from spatial transactions”, Journal of Intelligent Information Systems, vol. 28, pp. 1-22, 2007.
B. Furletti and Turini, F., “Knowledge Discovery in Ontologies”, Intelligent Data Analysis, vol. 16, 2012.
D. Pedreschi and Ruggieri, S., “On logic programs that always succeed”, Sci. Comput. Program., vol. 48, pp. 163-196, 2003.
D. Pedreschi and Ruggieri, S., “On Logic Programs That Do Not Fail”, Electr. Notes Theor. Comput. Sci., vol. 30, 1999.
M. Natilli, Rossi, A., Trecroci, A., Cavaggioni, L., Merati, G., and Formenti, D., “The long-tail effect of the COVID-19 lockdown on Italians’ quality of life, sleep and physical activity”, Scientific Data, vol. 9, pp. 1–10, 2022.
L. Leone, Marchitiello, M., Natilli, M., and Romano, M. Francesca, “Measuring the effectiveness of homeopathic care through objective and shared indicators”, Homeopathy, vol. 100, pp. 212–219, 2011.
G. Cornacchia and Pappalardo, L., “A Mechanistic Data-Driven Approach to Synthesize Human Mobility Considering the Spatial, Temporal, and Social Dimensions Together”, ISPRS International Journal of Geo-Information, vol. 10, p. 599, 2021.
C. Renso and Ruggieri, S., “A Mediator Approach for Representing Knowledge”, Intelligent Multimedia Presentation Systems. Human Computer Interaction Letters, 1 (1): 32-38, April 1998., 1998.
A. Rita Nogueira, Pugnana, A., Ruggieri, S., Pedreschi, D., and Gama, J., “Methods and tools for causal discovery and causal inference”, WIREs Data Mining Knowl. Discov., vol. 12, 2022.
D. Gambetta, Mauro, G., and Pappalardo, L., “Mobility Constraints in Segregation Models”, Scientific Reports, vol. 13, p. 12087, 2023.
R. Pellungrini, Pappalardo, L., Simini, F., and Monreale, A., “Modeling Adversarial Behavior Against Mobility Data Privacy”, IEEE Transactions on Intelligent Transportation SystemsIEEE Transactions on Intelligent Transportation Systems, pp. 1 - 14, 2020.
G. Cornacchia, Rossetti, G., and Pappalardo, L., “Modelling Human Mobility considering Spatial, Temporal and Social Dimensions”, arXiv preprint arXiv:2007.02371, 2020.
A. Brogi, Mancarella, P., Pedreschi, D., and Turini, F., “Modular Logic Programming”, ACM Trans. Program. Lang. Syst., vol. 16, pp. 1361-1398, 1994.
A. Monreale, Andrienko, G., Andrienko, N., Giannotti, F., Pedreschi, D., Rinzivillo, S., and Wrobel, S., “Movement Data Anonymity through Generalization”, Transactions on Data Privacy, vol. 3, pp. 91–121, 2010.
M. Berlingerio, Coscia, M., Giannotti, F., Monreale, A., and Pedreschi, D., “Multidimensional networks: foundations of structural analysis”, World Wide Web, vol. Volume 15 / 2012, 2012.
A. Romei and Ruggieri, S., “A multidisciplinary survey on discrimination analysis”, The Knowledge Engineering Review, vol. 29, pp. 582–638, 2014.
R. Trasarti, Guidotti, R., Monreale, A., and Giannotti, F., “MyWay: Location prediction via mobility profiling”, Information Systems, vol. 64, pp. 350–367, 2017.
G. Rossetti, Milli, L., Rinzivillo, S., Sirbu, A., Pedreschi, D., and Giannotti, F., “NDlib: a python library to model and analyze diffusion processes over complex networks”, International Journal of Data Science and Analytics, vol. 5, pp. 61–79, 2018.
G. Rossetti, Milli, L., Rinzivillo, S., Sirbu, A., Pedreschi, D., and Giannotti, F., “NDlib: a python library to model and analyze diffusion processes over complex networks”, International Journal of Data Science and Analytics, pp. 1–19, 2017.

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