Zhongli Ding successfully defended her Ph.D. dissertation entitled "BayesOWL: A Probabilistic Framework for Uncertainty in Semantic Web" on December 5, 2005. Dr. Ding came to UMBC in the Fall of 1999 after receiving her undergraduate degree from the University of Science and Technology of China in Hefei. She joined the ebquity lab in 2000 and has worked closely with Professor Yun Peng, who was her mentor and dissertation supervisor. She received a Masters degree in Computer Science in May 2001 and was admitted to candidacy in July 2003.

Through her dissertation research, Dr. Ding was one of the first researchers to address the problem of modeling uncertainty in Semantic Web languages. Her work followed a probabilistic approach and resulted in a theoretical framework that incorporates the Bayesian network model into the Semantic Web language OWL. This framework consists of three key components:

• a representation for encoding the probability distributions as OWL classes
• a set of structural translation rules and procedures that converts an OWL taxonomy ontology into a BN directed acyclic graph (DAG)
• a method SD-IPFP based on "iterative proportional fitting procedure" (IPFP) that incorporates available probability constraints into the conditional probability tables (CPTs) of the translated BN.