Bridging the gap between knowledge and learning

AC-Oberseminar talk by Dr. Michael Cochez on Tuesday (07.06.2022) from 14:00 to 15:00

Abstract

Machine learning approaches have been applied successfully in many fields, also in knowledge representation. For example, graph embedding techniques have been taken up by the community as a tool to solve various tasks. Also, other models which connect the knowledge representation, where information is typically represented in the form of a graph, with the machine learning world exist. In this talk, I will first give a high-level overview of the topics in the field of representation learning on knowledge graphs and then present our recent work on inductive representation learning and approximate graph query answering.

Speaker

Dr. Michael Cochez is an assistant professor in the Knowledge Representation and Reasoning group at the Vrije Universiteit Amsterdam and manager of the Discovery Lab (an ICAI lab in collaboration with Elsevier and the University of Amsterdam). He works on bridging the gap between machine learning and knowledge graphs. His research interests include embedding knowledge graphs for downstream machine learning tasks, dealing with missing information in graphs (link prediction, approximate graph query answering), and applications such as question answering and recommendations.

Event details

Date: June 7, 2022, 14:00 hours.
Place: Room UN32.122 in the Analytic Computing Building, or online via MS Teams.
Duration: 1 hour 30 minutes.

Recent related publications

  1. Generale, A., Blume, T., & Cochez, M. (2022). Scaling R-GCN Training with Graph Summarization.
    https://www2022.thewebconf.org/PaperFiles/158.pdf
  2. Alivanistos, D., Berrendorf, M., Cochez, M., & Galkin, M. (2021). Query Embedding on Hyper-relational Knowledge Graphs. In Proceedings of the Tenth International Conference on Learning Representations
    (ICLR 2022) https://openreview.net/forum?id=4rLw09TgRw9
  3. Arakelyan, E., Daza, D., Minervini, P., & Cochez, M. (2021). Complex query answering with neural link predictors. In Proceedings of the Ninth International Conference on Learning Representations (ICLR 2021,
    oral presentation)  arXiv preprint https://arxiv.org/abs/2011.03459
  4. Daza, D., Cochez, M., and Groth, P.. (2021). Inductive Entity Representations from Text via Link Prediction. In Proceedings of the Web Conference 2021 (WWW '21). Association for Computing Machinery,
    New York, NY, USA, 798–808. DOI: https://doi.org/10.1145/3442381.345014
  5. van Bakel, R., Aleksiev, T., Daza, D., Alivanistos, D., & Cochez, M. (2021). Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification. In Graph Structures for Knowledge Representation and Reasoning: 6th International Workshop, GKR 2020, Virtual Event, September 5, 2020, Revised Selected Papers 6 (pp.107-124). Springer International Publishing.
  6. Daza, D., & Cochez, M. (2020). Message passing query embedding. arXiv preprint arXiv:2002.02406.(presented at ICML Workshop - Graph Representation Learning and Beyond 2020)

 

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