Keynote: Question Answering over Temporal Knowledge Graphs
Temporal Knowledge Graphs (Temporal KGs) extend regular Knowledge Graphs by providing temporal scopes (start and end times) on each edge in the KG. While Question Answering over KG (KGQA) has received some attention from the research community, QA over Temporal KGs (Temporal KGQA) is a relatively unexplored area. Lack of broad coverage datasets has been another factor limiting progress in this area. In this talk, I shall present CronQuestions, a new dataset for temporal KGQA, and CronKGQA, a transformer-based approach for this problem which exploits recent advances in Temporal KG embeddings. Towards the end of the talk, I shall also present a quick overview of our recent work on Multilingual Fact Linking.
Bio
Partha Talukdar is a Research Scientist at Google Research, Bangalore where he leads a group focused on Natural Language Understanding. He is also an Associate Professor (on leave) at IISc Bangalore. He received his PhD (2010) in CIS from the University of Pennsylvania. Partha is broadly interested in Natural Language Processing, Machine Learning, and Knowledge Graphs. Partha is a recipient of several awards, including an Outstanding Paper Award at ACL 2019. He is a co-author of a book on Graph-based Semi-Supervised Learning.