Ismail Ilkan Ceylan
My research interests are broadly in AI & machine learning with a particular focus on relational learning & reasoning, which includes a class of challenging problems that can be naturally characterised on relational structures, such as graphs, knowledge bases, or more general logical representations. The goal is being able to more efficiently and reliably learn from relational patterns and reason over them. This is a highly interactive field, where techniques from knowledge representation (e.g., logical reasoning), machine learning (e.g., deep learning, graph representation learning, probabilistic inference), and theoretical computer science (complexity theory, graph theory) are relevant.
Explanations for Negative Query Answers under Existential Rules
İsmail İlkan Ceylan‚ Thomas Lukasiewicz‚ Enrico Malizia‚ Cristian Molinaro and Andrius Vaicenavičius
In Diego Calvanese and Esra Erdem, editors, Proceedings of 17th International Conference on Principles of Knowledge Representation and Reasoning‚ KR 2020‚ Rhodes‚ Greece‚ September 12−18‚ 2020. AAAI Press. September, 2020.
Explanations for Ontology−Mediated Query Answering in Description Logics
İsmail İlkan Ceylan‚ Thomas Lukasiewicz‚ Enrico Malizia and Andrius Vaicenavičius
In Giuseppe De Giacomo, editor, Proceedings of the 24th European Conference on Artificial Intelligence‚ ECAI 2020‚ Santiago de Compostela‚ Spain‚ June 8–12‚ 2020. IOS Press. June, 2020.
Neural Networks for Approximate DNF Counting: An Abridged Report
Ralph Abboud‚ İsmail İlkan Ceylan and Thomas Lukasiewicz
In Lingfei Wu‚ Jian Tang‚ Yinglong Xia and Charu Aggarwal, editors, Proceedings of the 1st International Workshop on Deep Learning on Graphs: Methodologies and Applications‚ DLGMA 2020‚ New York‚ New York‚ USA‚ February 8‚ 2020. February, 2020.