QDEF and Its Approximations in OBDM
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).
Cite
@article{arxiv.2108.10021,
title = {QDEF and Its Approximations in OBDM},
author = {Gianluca Cima and Federico Croce and Maurizio Lenzerini},
journal= {arXiv preprint arXiv:2108.10021},
year = {2021}
}
Comments
A more compact version of this paper will be published at the proceedings of the 30th ACM International Conference on Information and Knowledge Management. The associated DOI is: https://doi.org/10.1145/3459637.34824661