Speaker: Craig Knoblock
Title: Extracting, Aligning, and Linking Data to Build Knowledge Graphs
There is a tremendous amount of data spread across the web and stored in databases that can be turned into an integrated semantic network of data, called a knowledge graph. However, exploiting the available data to build knowledge graphs is difficult due to the heterogeneity of the sources, scale in the amount of data, and noise in the data. In this talk I will present our approach to building knowledge graphs, including acquiring data from online sources, extracting information from those sources, aligning and linking the data across sources, and building and querying knowledge graphs at scale. We applied our approach, implemented in a system called DIG, to a variety of challenging real-world problems including combating human trafficking by analyzing web ads, identifying illegal arms sales from online marketplaces, and predicting cyber attacks using data extracted from both the open and dark web.
Craig Knoblock is a Research Professor of both Computer Science and Spatial Sciences at the University of Southern California (USC), Research Director at the Information Sciences Institute, and Associate Director of the Informatics Program at USC. 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 300 journal articles, book chapters, and conference papers on these topics. Dr. Knoblock is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), Fellow of the Association of Computing Machinery (ACM), Senior Member of IEEE, past President and Trustee of the International Joint Conference on Artificial Intelligence (IJCAI), and winner of the 2014 Robert S. Engelmore Award.