III: Small: Efficient Query Processing over Large Probabilistic Knowledge Bases

Principal Investigator: Daisy Zhe Wang

Sponsor: National Science Foundation

Start Date: September 1, 2015

End Date: August 31, 2020

Amount: $499,917


Due to the uncertainty, incompleteness and inconsistency from automatic extraction processes, query results from current large-scale knowledge bases (KBs) are incomplete, erroneous and conflicting. The research objective of this proposal is to extend the state-of-the-art KB systems to create a probabilistic first-order KB system that can infer missing knowledge using rules, prune conflicting knowledge using constraints, and return confidence values for resulting tuples. The new system and algorithms developed in this proposal can enable advanced online data analysis through an declarative query interface over large uncertain graphs exist in many high impact applications, including knowledge bases, social networks, and biological networks.

More Information: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1526753&HistoricalAwards=false