Program
The workshop is from 1:00pm to 5:00pm on June 25, 2017. Paper presentations will be for 20 minutes with 15 minutes for presentation and 5 minutes for discussion.
Location: 2nd Floor, Board Room, Franklin Plaza. 4 4th Street, Troy NY 12180 USA. https://goo.gl/maps/FQKJNGzD7PF2
Registration: If you are planning to attend only the workshop, please contact Kathy Fontaine (fontak@rpi.edu) and request her to send you a workshop only registration link. If you plan to attend the entire WebSci'17 conference, please register here: https://websci.tw.rpi.edu/Register.html
| 1:00 - 1:10 |
Opening and welcome |
| 1:10 - 2:10 |
Keynote Talk: Prof. Craig Knoblock (USC). Extracting, Aligning, and Linking Data to Build Knowledge Graphs |
| 2:10 - 2:30 |
Agniva Banerjee, Raka Dalal, Sudip Mittal and Karuna Joshi. Generating Digital Twin models using Knowledge Graphs for Industrial Production Lines |
| 2:30 - 3:00 |
Coffe Break |
| 3:00 - 3:30 |
Invited Talk: Arun Subramaniyan (VP-Data & Analytics, GE Oil & Gas). Title TBD |
| 3:30 - 3:50 |
Roopteja Muppalla, Sarasi Lalithsena, Tanvi Banerjee and Amit Sheth. A Knowledge Graph Framework for Detecting Traffic Events Using Stationary Cameras |
| 3:50 - 4:10 |
Sabbir Rashid, Amar Viswanathan, Ian Gross, Elisa Kendall and Deborah McGuinness. Leveraging Semantics for Large-Scale Knowledge Graph Evaluation |
| 4:10 - 4:20 |
Haklae Kim. Enterprise Knowledge Graph for Consumer Electronics |
| 4:20 - 5:00 |
Panel Discussion: Industrial Knowledge Graphs: Opportunities and Challenges
Panel Members: Craig Knoblock, University of Southern California; Andy Crapo, GE Global Research; Achille Fokoue, IBM T.J Watson; Evren Sirin, Stardog; Arun Subramaniyan, GE Oil & Gas
Moderator: Justin McHugh, GE Global Research
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Keynote Talk: Extracting, Aligning, and Linking Data to Build Knowledge Graphs
There is a huge amount of data spread across the web and stored in databases that we can use to build knowledge graphs. However, exploiting this 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 the knowledge graphs at scale. We applied our approach, implemented in a system called DIG, to a number 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 250 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), a Distinguished Scientist of the Association of Computing Machinery (ACM), a 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.
