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LeMuR

Funded project (January 2023 - December 2026)

Learning with Multiple Representations

About the project

LeMuR is an MSCA (Marie Skłodowska-Curie Actions) Doctoral Network (DN) 2021 on Learning with Multiple Representations. The goal of LeMuR is to develop the theoretical foundations and a first set of algorithms for the new “Learning with Multiple Representations” (LMR) paradigm. Moreover, corresponding applications will be developed to demonstrate the usefulness of the new family of approaches.

Specifically, LMR algorithms will allow flexible representations (e.g., suitable for explainability, fairness, …) with diverse target functions (e.g., incorporating environmental or even social impact) so as to make the induced models abide by the Green Charter and trustworthy AI criteria by design. The project will focus on learning with weak supervision because it addresses one of the major flaws of modern ML approaches, i.e., their data hunger, by means of weaker sources of labeling for training data. The outcome of the DN will be a set of 10 experts trained to implement the third and subsequent waves of AI in Europe. The highly interdisciplinary and intersectoral context in which they will be trained will provide them with research-related and transferable competencies relevant to successful careers in central AI areas.

Publications

Ontolearn---A Framework for Large-scale OWL Class Expression Learning in Python

By Caglar Demir, Alkid Baci, N'Dah Jean Kouagou, Leonie Nora Sieger, Stefan Heindorf, Simon Bin, Lukas Blübaum, Alexander Bigerl, Axel-Cyrille Ngonga Ngomo

Journal of Machine Learning Research, JMLR, 2025, #article

ROCES: Robust Class Expression Synthesis in Description Logics via Iterative Sampling

By N'Dah Jean Kouagou, Stefan Heindorf, Caglar Demir, Axel-Cyrille Ngonga Ngomo

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24, 2024, #inproceedings

Universal Knowledge Graph Embeddings

By N'Dah Jean Kouagou, Caglar Demir, Hamada M. Zahera, Adrian Wilke, Stefan Heindorf, Jiayi Li, Axel-Cyrille Ngonga Ngomo

Companion Proceedings of the ACM on Web Conference 2024, 2024, #inproceedings

Embedding Knowledge Graphs in Degenerate Clifford Algebras

By Louis Mozart Kamdem Teyou, Caglar Demir, Axel-Cyrille Ngonga Ngomo

Proceedings of the 27TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2024, #inproceedings

Embedding Knowledge Graphs in Function Spaces

By Louis Mozart Kamdem Teyou, Caglar Demir, Axel-Cyrille Ngonga Ngomo

Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024, #inproceedings

Ontolearn---A Framework for Large-scale OWL Class Expression Learning in Python

By Caglar Demir, Alkid Baci, N'Dah Jean Kouagou, Leonie Nora Sieger, Stefan Heindorf, Simon Bin, Lukas Blübaum, Alexander Bigerl, Axel-Cyrille Ngonga Ngomo

Journal of Machine Learning Research, JMLR, 2025, #article

ROCES: Robust Class Expression Synthesis in Description Logics via Iterative Sampling

By N'Dah Jean Kouagou, Stefan Heindorf, Caglar Demir, Axel-Cyrille Ngonga Ngomo

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24, 2024, #inproceedings

Universal Knowledge Graph Embeddings

By N'Dah Jean Kouagou, Caglar Demir, Hamada M. Zahera, Adrian Wilke, Stefan Heindorf, Jiayi Li, Axel-Cyrille Ngonga Ngomo

Companion Proceedings of the ACM on Web Conference 2024, 2024, #inproceedings

Embedding Knowledge Graphs in Degenerate Clifford Algebras

By Louis Mozart Kamdem Teyou, Caglar Demir, Axel-Cyrille Ngonga Ngomo

Proceedings of the 27TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2024, #inproceedings

Embedding Knowledge Graphs in Function Spaces

By Louis Mozart Kamdem Teyou, Caglar Demir, Axel-Cyrille Ngonga Ngomo

Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024, #inproceedings