NSF Convergence Accelerator Tracks A&B Speaker Series

 

Knowledge Graphs for AI: Wikidata and Beyond

 

Markus Krötzsch

Technische Universität Dresden

Wednesday, Feb 3, 2021. 9:00 a.m. (PT)

The recorded video of this talk can be found here.

 

The National Science Foundation’s (NSF) tracks A and B of the Convergence Accelerator program are proud to present the first speaker in their 2021/22 speaker series on Open Knowledge Networks. The series will feature researchers and practitioners widely recognized for their contribution to knowledge graphs, knowledge engineering, and FAIR data

Abstract. Wikidata, the knowledge graph of Wikimedia, has successfully grown from an experimental “data wiki” to a well-organized reference knowledge base with a large and active editor community as well as many academic and industrial uses. It is also a key ingredient of popular AI applications, most prominently of intelligent agents such as Apple’s Siri or Amazon’s Alexa. Of course, human knowledge is fully expected to be in high demand in this time of rapidly advancing AI. And yet, the fact that modern AI relies on the manual labor of thousands of human knowledge modelers is in stark contrast to the common narrative of AI in popular media, which tells us that methods of pattern recognition and statistical function approximation can produce intelligent behavior from unstructured data without much human intervention. However, Wikidata is not a singular exception to the trend but rather a specific solution to a general need of AI: the need for knowledge that is understandable to humans and accessible to computers. Almost every major AI application incorporates such knowledge, and organizations long have realized the need to acquire and develop knowledge resources as part of their AI strategy. The next frontier in AI is the ability of systems to explain and justify their behavior. There, too, we can see the need for knowledge-based technologies as a bridge between human understanding and computational mechanisms, but the task goes far beyond the realms of knowledge representation or machine learning, and will require the effort of all of AI and maybe all of computer science. In my talk, I will give an overview of Wikidata and outline some ongoing research efforts that combine knowledge representation with other methods towards the creation of (more) understandable and accountable AI.

Bio: Markus Krötzsch is a full professor at the Faculty of Computer Science of TU Dresden, where he is holding the chair for Knowledge-Based Systems. He obtained his Ph.D. from Karlsruhe Institute of Technology (KIT) in 2010, and thereafter worked at the Department of Computer Science of the University of Oxford until October 2013. He has contributed to the concept and design of Wikidata, as one of the most prominent examples of applied knowledge representation today. His research made many further contributions to the development and analysis of knowledge modeling languages (including the W3C OWL standard), inference methods, and automated reasoners. Krötzsch is a member of the Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) and of the Center for Perspicuous Computing (CPEC).

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NSF Convergence Accelerator Series Tracks A&B: Markus Krötzsch