
NIMI | InfAI
Nature-Inspired Machine Intelligence
Nature, and all that it encompasses, has influenced computer science, in particular Artificial Intelligence, from its inception. Many effective tools, mechanisms, processes, algorithms, methods, and systems have been proposed inspired by nature. For example, Neural Networks are roughly inspired by the cognitive brain function, Genetic Algorithms are inspired by evolution and the survival of the fittest, and Artificial Immune Systems are inspired by their biological equivalents. Further examples include swarm or collective approaches, that are inspired by colonies of insects and birds. Current AI methods have the following weaknesses:
- The power efficiency of AI systems is very low — a brain just needs a few watts compared to supercomputer/clouds, and
- Systems are often low-level end-to-end and cannot incorporate knowledge very well, however, most of the intelligence of humans comes from building layer after layer of knowledge
- Systems often lack the robustness and lifelong learning abilities we see in nature
„The job of a scientist is to listen carefully to nature, not to tell nature how to behave.“
Richard P. Feynman
Research in machine intelligence inspired by natural science can result in innovations that address those weaknesses. The main activities of the group and planned research directions will focus on existing concepts in nature and natural science including intelligent systems such as the human brain. Within the group, the following focal points will be addressed in the next years:
- Nature in Knowledge Representation — Representation Learning and Reasoning
- Natural Sciences in Knowledge Discovery and Data Mining with Embeddings/Neural Networks
- Human Mind in Deep Neural-symbolic Learning and Reasoning
- Nature-inspired Neural Networks
- Applications of Machine Intelligence for Social good, Scholarly Communication and Education, Health, and Nature and Environmental Studies
MEET NIMI TEAM
PROJECTS
SELECTED PUBLICATIONS
5* Knowledge Graph Embeddings with Projective Transformations Konferenzbeitrag
In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021, S. 9064–9072, AAAI Press, 2021.
Unveiling Scholarly Communities over Knowledge Graphs Konferenzbeitrag
In: Méndez, Eva; Crestani, Fabio; Ribeiro, Cristina; David, Gabriel; Lopes, João Correia (Hrsg.): Digital Libraries for Open Knowledge, 22nd International Conference on Theory and Practice of Digital Libraries, TPDL 2018, Porto, Portugal, September 10-13, 2018, Proceedings, S. 103–115, Springer, 2018.
OpenResearch: Collaborative Management of Scholarly Communication Metadata Konferenzbeitrag
In: Blomqvist, Eva; Ciancarini, Paolo; Poggi, Francesco; Vitali, Fabio (Hrsg.): Knowledge Engineering and Knowledge Management - 20th International Conference, EKAW 2016, Bologna, Italy, November 19-23, 2016, Proceedings, S. 778–793, 2016.
SEE ALSO
Design and Development of Murphy System: Generating Meaningful Negative Samples for KGEs Abschlussarbeit University of Bonn, Germany, 2022. Towards Hybrid Logic-based and Embedding-based Reasoning on Financial
Knowledge Graphs Konferenzbeitrag In: Ramanath, Maya; Palpanas, Themis (Hrsg.): Proceedings of the Workshops of the EDBT/ICDT 2022 Joint Conference,
Edinburgh, UK, March 29, 2022, CEUR-WS.org, 2022. Data science with Vadalog: Knowledge Graphs with machine learning and reasoning in practice Artikel In: Future Gener. Comput. Syst., Bd. 129, S. 407–422, 2022. Going Beyond the Paradigm of Knowledge Graph Embedding Models Abschlussarbeit University of Bonn, 2022. Sleeping Beauties of Coronavirus Research Artikel In: IEEE Access, Bd. 9, S. 21192–21205, 2021. Link Prediction of Weighted Triples for Knowledge Graph Completion
Within the Scholarly Domain Artikel In: IEEE Access, Bd. 9, S. 116002–116014, 2021.
Open THESIS
Finished THESIS
Design and Development of Murphy System: Generating Meaningful Negative Samples for KGEs Abschlussarbeit
University of Bonn, Germany, 2022.
Going Beyond the Paradigm of Knowledge Graph Embedding Models Abschlussarbeit
University of Bonn, 2022.
Unveiling the Effect of using Moebius Transformations on Knowledge Graph Embeddings Abschlussarbeit
University of Bonn, Germany , 2020.
VACANCIES
Research Areas


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