# Knowledge Refinement via Rule Selection

## TL;DR

This paper investigates the computational complexity of selecting optimal rule subsets for knowledge refinement, focusing on minimizing errors in data transformation and entity resolution tasks, and explores bi-objective optimization challenges.

## Contribution

It provides a systematic complexity-theoretic analysis of rule selection problems, establishing hardness results and exploring bi-objective optimization complexities.

## Key findings

- Decision problems are computationally hard (NP-hard, DP-complete).
- Approximation bounds are established for the minimization problem.
- Bi-objective optimization testing is DP-complete.

## Abstract

In several different applications, including data transformation and entity resolution, rules are used to capture aspects of knowledge about the application at hand. Often, a large set of such rules is generated automatically or semi-automatically, and the challenge is to refine the encapsulated knowledge by selecting a subset of rules based on the expected operational behavior of the rules on available data. In this paper, we carry out a systematic complexity-theoretic investigation of the following rule selection problem: given a set of rules specified by Horn formulas, and a pair of an input database and an output database, find a subset of the rules that minimizes the total error, that is, the number of false positive and false negative errors arising from the selected rules. We first establish computational hardness results for the decision problems underlying this minimization problem, as well as upper and lower bounds for its approximability. We then investigate a bi-objective optimization version of the rule selection problem in which both the total error and the size of the selected rules are taken into account. We show that testing for membership in the Pareto front of this bi-objective optimization problem is DP-complete. Finally, we show that a similar DP-completeness result holds for a bi-level optimization version of the rule selection problem, where one minimizes first the total error and then the size.