On Constrained Open-World Probabilistic Databases

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.

Authors

• Tal Friedman
• Guy Van den Broeck University of California, Los Angeles

Keywords

• Multidisciplinary Topics and Applications: Databases
• Uncertainty in AI: Approximate Probabilistic Inference
• Uncertainty in AI: Exact Probabilistic Inference
• Uncertainty in AI: Relational Inference