# On Constrained Open-World Probabilistic Databases

## TL;DR

This paper addresses the challenge of answering queries in open-world probabilistic databases by introducing constraints to improve precision, proposing algorithms, hardness results, and approximations for different query classes.

## Contribution

It introduces a constrained approach to open-world probabilistic databases, enhancing query accuracy and efficiency with new algorithms and complexity analysis.

## Key findings

- Proposed an algorithm for a class of queries.
- Established a hardness result for another query class.
- Developed an efficient approximation for a broad query class.

## Abstract

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.