Building and Using Knowledge Graphs to Turn Data into Knowledge - Craig Knoblock, USC

Conference Website: http://saiconference.com/Computing

Abstract: Creating knowledge graphs from data provides a way of combining sources of information in ways that can then be exploiting to solve various real-world problems. However, the challenge in building knowledge graphs is getting the data into a usable form. In this talk I will highlight some of the techniques we have developed for ingesting data into a knowledge graph, including automatic techniques for finding errors in tables and methods for understanding the content of a given data source. I will also describe some of the applications we have developed using knowledge graphs and how we were able to transform challenging tasks into ones that could be addressed, including combating human trafficking, identifying illegal arms sales, and predicting cyber attacks.

Craig Knoblock is the Keston Executive Director of the Information Sciences Institute, Research Professor of both Computer Science and Spatial Sciences, and Vice Dean of Engineering at the University of Southern California. He received his Bachelor of Science degree from Syracuse University and his Master’s and Ph.D. from Carnegie Mellon University in computer science. His research focuses on techniques for describing, acquiring, and exploiting the semantics of data. He has worked extensively on source modeling, schema and ontology alignment, entity and record linkage, data cleaning and normalization, extracting data from the web, and combining these techniques to build knowledge graphs. He has published more than 400 journal articles, book chapters, and conference and workshop papers on these topics and has received 7 best paper awards on this work. He also co-authored a recent book titled Knowledge Graphs Fundamentals, Techniques, and Applications, which was published in 2021 by MIT Press. Dr. Knoblock is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), the Association of Computing Machinery (ACM), and the Institute of Electrical and Electronic Engineers (IEEE). He is also past President of the International Joint Conference on Artificial Intelligence (IJCAI) and winner of the Robert S. Engelmore Award.