A Hitchhiker's Guide to Ontology / Fabian Suchanek (Télécom Paris)
Channel: Web IR / NLP Group at NUS
Date: 2023-07-05

Abstract:
Language Models have brought major breakthroughs in natural language processing. Notwithstanding this success, I will show that certain applications still need symbolic representations. I will then show how different methods (language models and others) can be harnessed to build such symbolic representations. I will also introduce our main project in this direction, the YAGO knowledge base. I will then talk about the incompleteness of knowledge bases. We have developed several techniques to estimate how much data is missing in a knowledge base, as well as rule mining methods to derive that data. I will then present our work on efficient querying of knowledge bases. Finally, I will talk about applications of knowledge bases in the domain of speech analysis and the digital humanities, as well as about our methods for explainable AI.

Bio:
Fabian M. Suchanek is a full professor at the engineering school Telecom Paris in France. He obtained his doctorate at the Max-Planck Institute for Computer Science in Germany. In his thesis, Fabian notably developed the YAGO knowledge base, one of the largest public knowledge bases, which earned him an honorable mention of the SIGMOD thesis prize, as well as, 10 years later, the “Test of Time” from the WWW 2018 conference. Fabian was a postdoctoral fellow at Microsoft Research in Silicon Valley and at INRIA Saclay. Then he led a research group at the Max-Planck Institute for Computer Science. Since 2013, he has been an associate professor at Télécom Paris in France, and since 2016 a full professor. Fabian teaches courses on natural language processing, information extraction, and knowledge representation. With his students, he works on information extraction, rule mining, ontology matching and other topics related to large knowledge bases. He has published over 100 scientific papers, and his work has been cited over 15,000 times.

Aired Live: 5 July 2023