We study a novel reasoning task in Ontology-based Data Management (OBDM), called Abstraction, which aims at associating formal semantic descriptions to data services. In OBDM a domain ontology is used to provide a semantic layer mapped to the data sources of an organization. The basic idea of the work presented in this paper is to explain the semantics of a data service in terms of a query over the ontology. We illustrate a formal framework for this problem, based on three different notions of abstraction, called sound, complete, and perfect, respectively. We present a thorough complexity analysis of two computational problems, namely verification (checking whether a query is an abstraction of a given data service), and computation (computing an abstraction of a given data service).

The notion of Abstraction in Ontology-based Data Management / Cima, G; Poggi, A; Lenzerini, M. - In: ARTIFICIAL INTELLIGENCE. - ISSN 0004-3702. - 323:(2023). [10.1016/j.artint.2023.103976]

The notion of Abstraction in Ontology-based Data Management

Cima, G
Primo
;
Poggi, A
Secondo
;
Lenzerini, M
Ultimo
2023

Abstract

We study a novel reasoning task in Ontology-based Data Management (OBDM), called Abstraction, which aims at associating formal semantic descriptions to data services. In OBDM a domain ontology is used to provide a semantic layer mapped to the data sources of an organization. The basic idea of the work presented in this paper is to explain the semantics of a data service in terms of a query over the ontology. We illustrate a formal framework for this problem, based on three different notions of abstraction, called sound, complete, and perfect, respectively. We present a thorough complexity analysis of two computational problems, namely verification (checking whether a query is an abstraction of a given data service), and computation (computing an abstraction of a given data service).
2023
abstraction; ontology-based data management; computational complexity
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The notion of Abstraction in Ontology-based Data Management / Cima, G; Poggi, A; Lenzerini, M. - In: ARTIFICIAL INTELLIGENCE. - ISSN 0004-3702. - 323:(2023). [10.1016/j.artint.2023.103976]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1691772
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