[Announcement] Talk: Knowledge Graphs for AI: Wikidata and Beyond

  *NSF Convergence Accelerator Tracks A&B Speaker Series*

"Knowledge Graphs for AI: Wikidata and Beyond"

by Markus Krötzsch
Technische Universität Dresden

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

Required Zoom registration before the event: 
https://ucsb.zoom.us/meeting/register/tZYuc-qvpj4iHdK1gehCPRRYlBEaCyo44e9v

Upon registration, you will receive a confirmation email with Zoom login 
details.

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).

Weblink: http://spatial.ucsb.edu/2021/Markus-Kr%C3%B6tzsch

Please contact us (http://spatial.ucsb.edu/contact/) for follow-up 
questions.

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Krzysztof Janowicz

Geography Department, University of California, Santa Barbara
4830 Ellison Hall, Santa Barbara, CA 93106-4060

Email: jano@geog.ucsb.edu
Webpage: http://geog.ucsb.edu/~jano/
Semantic Web Journal: http://www.semantic-web-journal.net

Received on Sunday, 31 January 2021 22:13:34 UTC