QDEF and Its Approximations in OBDM

TL;DR

This paper introduces approximations for query definability in Ontology-based Data Management, analyzes their complexity, and explores verification, existence, and computation problems related to dataset characterizations.

Contribution

It proposes new approximation methods for perfect dataset characterizations and provides a detailed complexity analysis of related computational problems.

Findings

Approximate characterizations improve dataset query matching.

Complexity results inform the feasibility of verification, existence, and computation tasks.

Thorough analysis guides future research in OBDM query definability.

Abstract

Given an input dataset (i.e., a set of tuples), query definability in Ontology-based Data Management (OBDM) amounts to find a query over the ontology whose certain answers coincide with the tuples in the given dataset. We refer to such a query as a characterization of the dataset with respect to the OBDM system. Our first contribution is to propose approximations of perfect characterizations in terms of recall (complete characterizations) and precision (sound characterizations). A second contribution is to present a thorough complexity analysis of three computational problems, namely verification (check whether a given query is a perfect, or an approximated characterization of a given dataset), existence (check whether a perfect, or a best approximated characterization of a given dataset exists), and computation (compute a perfect, or best approximated characterization of a given dataset).