Applying Noisy Knowledge Graphs to Real Problems

Applying Noisy Knowledge Graphs to Real Problems

Wednesday, May 22
2:00 PM - 2:40 PM
Marquis Salon 14

Knowledge graphs (KGs) have recently emerged as a powerful way to represent knowledge in multiple communities, including data mining, natural language processing and machine learning. Large-scale KGs like Wikidata and DBpedia are openly available, while in industry, the Google Knowledge Graph is a good example of proprietary knowledge that continues to fuel impressive advances in Google's semantic search capabilities. Yet, both crowdsourced and automatically constructed KGs suffer from noise, both during KG construction and during search and inference. In this talk, I will discuss how to build and use such knowledge graphs effectively, despite the noise and sparsity of labeled data, to solve real-world social problems such as providing insights in disaster situations, and helping law enforcement fight human trafficking. I will conclude by providing insight on the lessons learned, and the applicability of research techniques to industrial problems. The talk will be designed to appeal both to business and technical leaders.

Presentation Video


Mayank Kejriwal
Research Scientist, Research Assistant Professor
University of Southern California
Mayank Kejriwal is a research scientist at the University of Southern California's Information Sciences Institute (ISI), and a research assistant professor in the Department of Industrial and Systems Engineering. He received his Ph.D. from the University of Texas at Austin. His dissertation involved Web-scale data linking, and in addition to being published as a book, was recently recognized with an international Best Dissertation award in his field. His research is highly applied and sits at the intersection of knowledge graphs, social networks, Web semantics, network science, data integration and AI for social good. He has contributed to systems used by both DARPA and by law enforcement, and has active collaborations across academia and industry. He is currently co-authoring a textbook on knowledge graphs (MIT Press, 2018), and has delivered tutorials and demonstrations at numerous conferences and venues, including top academic venues such as KDD, AAAI, and ISWC, and industrial venues . He is currently serving as general chair of the ACM K-CAP conference in 2019, and is co-editing a special issue on knowledge graphs in the Semantic Web Journal. He was awarded a Key Scientific Challenges award in 2018 by the Allen Institute for Artificial Intelligence, and was recently named a Forbes Under 30 Scholar. He has also been nominated as a 2019 Forbes 30 Under 30 in the Science category.