Work

QDEF and Its Approximations in OBDM

Year: 2021

Type: preprint

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).

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2108.10021

Authors: Cima, Gianluca, Croce, Federico, Lenzerini, Maurizio

Institutions: Centre National de la Recherche Scientifique, Université de Bordeaux, Sapienza University of Rome

Language: English

Topic: Semantic Web and Ontologies

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, Agence Nationale de la Recherche, Ministero dell’Istruzione, dell’Università e della Ricerca, Horizon 2020 Framework Programme

Awards: G15967578, Intelligent handling of imperfect data, G2599072453, G2742610219, G3010527862, G3827193553, G6281959833, G6315092050, HOPE - High quality Open data Publishing and Enrichment, Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization, G895707134

OpenAlex ID: W3193882774

Cites: 36

Cited by: 1

Related to: 20