In this talk, I summarize the major results from BayesStore, a probabilistic database system that natively supports SML models and various inference algorithms to perform advanced statistical data analysis, including knowledge extraction from text. Results show that using in-database SML methods to extract and query probabilistic knowledge can significantly improve performance and answer quality.
Build on BayesStore, I propose to build a probabilistic knowledge base (ProbKB) system with a deep integration of the SML methods with scalable data processing frameworks. A ProbKB system should be designed to support various aspects in the life of a knowledge base (KB) including KB extraction, expansion, evolution, and integration. I will discuss in detail the challenges and our current progress in the following three research directions: (1) scalable statistical information extraction; (2) probabilistic deductive inference and incremental maintenance over large uncertain KBs; and (3) probabilistic knowledge integrate from both SML and crowd-sourcing.
Bio: Daisy Zhe Wang is an Assistant Professor in the CISE department at the University of Florida. She obtained her Ph.D. degree from the EECS Department at the University of California, Berkeley in 2011. Her research interest is in building systems and designing algorithms to support "better" data analysis, where "better" can mean: more efficient/scalable, more advanced, more accurate, more interactive, self-improving, and easier to use. She is particularly interested in bridging data management systems with statistical and probabilistic models and tools. She currently pursues research topics such as probabilistic databases, probabilistic knowledge bases, large-scale statistical text analysis, query-driven interactive machine learning, and crowd assisted machine learning.
Refreshments will be provided at 10:30 AM