Knowledge-Based News Event Analysis and Forecasting

Dr. Oktie Hassanzadeh

Bio: Dr. Oktie Hassanzadeh is a Senior Research Staff Member at IBM T.J. Watson Research Center. He is the recipient of several academic and corporate awards, including a top prize at the FinCausal-2022 Shared Task, a top prize at the Semantic Web Challenge at ISWC conference, and two best-paper awards at ESWC conferences. He has received his M.Sc. and Ph.D. degrees from the University of Toronto, where he received the IBM PhD fellowship and the Yahoo! Key Scientific Challenges awards. He is also a two-time recipient of the first prize at the Triplification Challenge at the SEMANTiCS Conference for his projects in the areas of Semantic Technologies and Linked Data. For more information, refer to his home page:

Title: Knowledge-Based News Event Analysis and Forecasting

Abstract: In this talk, I will present our ongoing work at IBM Research on building a toolkit for news event analysis and forecasting. The toolkit is powered by a Knowledge Graph (KG) of events curated from structured and textual sources of event-related knowledge. The toolkit provides functions for 1) mapping ongoing news headlines to concepts in the KG, 2) retrieval, reasoning, and visualization for causal analysis and forecasting, and 3) extraction of causal knowledge from text documents to augment the KG with additional domain knowledge. Each function has a number of implementations using state-of-the-art neuro-symbolic techniques. I will go over a number of use cases for the toolkit, including use cases in finance and enterprise risk management.