Topics

Hybrid Learning and Reasoning

Seminar an der Universität des Saarlandes, Fachrichtung Informatik, LSF 120889

The topics for seminar time slots (see schedule) are listed below. Currently assigned topics are marked in red.

Selected background literature is indicated below; topic reference papers are available in the web or on request from seminar organizers.

Topic#	Topic
1	Learning with symbolic semantic prior I H. Wang, D. Dou, and D. Lowd (2016): Ontology-Based Deep Restricted Boltzmann Machine. Proceedings of International Conference on Database and Expert Systems Applications (DEXA); LNCS, Springer. N. Phan, D. Dou, H. Wang, D. Kil, and B. Piniewski (2015): Ontology-Based Deep Learning for Human Behavior Prediction in Health Social Networks. Proceedings of 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics; ACM.
2	Learning with symbolic semantic prior II I. Donadello, L. Serafini & A.A.A. d’Avila Garcez (2017): Logic Tensor Networks for Semantic Image Interpretation. Proceedings of 26^th International Joint Conference on Artificial Intelligence (IJCAI).
3	Learning with symbolic semantic prior III J. Xu, Z. Zhang, T. Friedman, Y. Liang & G. Van den Broeck (2018): A Semantic Loss Function for Deep Learning with Symbolic Knowledge. Proceedings of 35^th International Conference on Machine Learning (ICML)
4	Explainable learning with symbolic semantic prior Md. Kamruzzaman Sarker et al. (2017): Explaining Trained Neural Networks with Semantic Web Technologies: First Steps. Proceedings of 12^th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy); CEUR Vol. 2003, I. Tiddi, M. d’Aquin, & E. Motta (2015): Data Patterns Explained with Linked Data. Proceedings Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD). Springer. See also: http://swsa.semanticweb.org/sites/default/files/tiddi-thesis_0.pdf [Ch. 3]
5	Explainable learning through inspection by reasoning J. Chen, F. Lécué, J. Pan, I. Horrocks, & H. Chen (2018): Knowledge-based Transfer Learning Explanation. Proceedings of 16^th International Conference on Principles of Knowledge Representation and Reasoning (KR). AAAI.
6	Learning intermediate symbolic abstraction for learning A.D. Garcez, A.R.R. Dutra & E. Alonso (2018): Towards Symbolic Reinforcement Learning with Common Sense. In: CoRR abs/1804.08597 M. Garnelo, K. Arulkumaran, & M. Shanahan (2016): Towards Deep Symbolic Reinforcement Learning. In: CoRR abs/1609.05518.
7	Meta-reasoning for learning to reason I M. Ebrahimi, M.K. Sarker, F. Bianchi, N. Xie, D. Doran & P. Hitzler (2018): Reasoning over RDF Knowledge Bases using Deep Learning. arXiv preprint arXiv:1811.04132
8	Meta-reasoning for learning to reason II Z. Du, C. Zhou, M. Ding, H. Yang, & J. Tang, J. (2019): Cognitive Knowledge Graph Reasoning for One-Shot Relational Learning. arXiv preprint arXiv:1906.05489
9	Meta-reasoning for learning to reason III P. Hohenecker & T. Lukasiewicz (2017): Deep Learning for Ontology Reasoning. arXiv preprint arXiv:1705.10342

Selected background references:

F. van Harmelen, A.T. Teije (2019). A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems.Journal of Web Engineering, 18(1-3):97-124, Rivers; also at https://arxiv.org/pdf/1905.12389
A.D. Garcez et al. (2019). Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning. arXiv preprint arXiv:1905.06088. https://arxiv.org/pdf/1905.06088
Tarek R. Besold et al. /2017): Neural-Symbolic Learning and Reasoning: A Survey and Interpretation.
https://arxiv.org/pdf/1711.03902
P. Hitzler, F. Bianchi, M. Ebrahimi & M.K. Sarker (2019): Neural-Symbolic Integration and the Semantic Web. http://www.semantic-web-journal.net/system/files/swj2188.pdf
International Workshop Series on Neural-Symbolic Learning and Reasoning (since 2005) and other related material: http://www.neural-symbolic.org/
International Workshop Series on Semantic Deep Learning (2017a, 2017b, 2018, 2019)
Deep Learning:

I.J. Goodfellow, Y. Bengio, A. Courville (2016): Deep Learning. MIT Press,
M. Nielsen (2017): Neural Networks and Deep Learning. http://neuralnetworksanddeeplearning.com/index.html
L. Deng (2014): A Tutorial Survey of Architectures, Algorithms, and Applications for Deep Learning. APSIPA Transactions on Signal and Information Processing, 3, e2.
http://deeplearning.net

Semantic Web:

P. Hitzler, M. Kroetzsch, S. Rudolph (2009): Foundations of Semantic Web Technologies. Chapman & Hall/CRC. http://www.semantic-web-book.org/page/Foundations_of_Semantic_Web_Technologies
A. Dengel (Hrsg.) (2012): Semantische Technologien - Grundlagen, Konzepte, Anwendungen. Springer

Impressum