Fabian M. Suchanek (Telecom Paris) - Data Science
Date: - Location: Eurecom
Abstract : Knowledge bases are an important asset in many of today's AI-based applications, including personal assistants and search engines. In this talk, I will give an overview of our recent work in this area. I will first talk about our main project, the YAGO knowledge base. In this context, I will present different methods of information extraction, in particular also the extraction of beliefs and causal relationships. 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 computational creativity and the digital humanities, as well as about our methods for explainable AI. Bio : Fabian M. Suchanek is a full professor at the Telecom Paris University in France. Fabian developed inter alia the YAGO knowledge base, one of the largest public general-purpose knowledge bases. This earned him a honorable mention of the SIGMOD dissertation award and the Test of Time Award of The Web Conference (WWW 2018). His interests include information extraction, automated reasoning, and knowledge bases. Fabian has published more than 100 scientific articles, among others at ISWC, VLDB, SIGMOD, WWW, CIKM, ICDE, and SIGIR, and his work has been cited more than 12,000 times. Data Science Seminars: https://ds.eurecom.fr/seminars/ https://mediaserver.eurecom.fr/channels/#data-science-seminars (internal)
