On Constrained Open-World Probabilistic Databases
On Constrained Open-World Probabilistic Databases
Tal Friedman, Guy Van den Broeck
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 5722-5729.
https://doi.org/10.24963/ijcai.2019/793
Increasing amounts of available data have led to a
heightened need for representing large-scale probabilistic knowledge bases. One approach is to
use a probabilistic database, a model with strong
assumptions that allow for efficiently answering
many interesting queries. Recent work on open-world probabilistic databases strengthens the semantics of these probabilistic databases by discarding the assumption that any information not present
in the data must be false. While intuitive, these
semantics are not sufficiently precise to give reasonable answers to queries. We propose overcoming these issues by using constraints to restrict this
open world. We provide an algorithm for one class
of queries, and establish a basic hardness result for
another. Finally, we propose an efficient and tight
approximation for a large class of queries.
Keywords:
Uncertainty in AI: Approximate Probabilistic Inference
Uncertainty in AI: Exact Probabilistic Inference
Multidisciplinary Topics and Applications: Databases
Uncertainty in AI: Relational Inference