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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 5: AAAI-22 Technical Tracks 5

Monotone Abstractions in Ontology-Based Data Management

February 1, 2023

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Authors

Gianluca Cima

CNRS University of Bordeaux


Marco Console

Sapienza University of Rome


Maurizio Lenzerini

Sapienza University of Rome


Antonella Poggi

Sapienza University of Rome


DOI:

10.1609/aaai.v36i5.20495


Abstract:

In Ontology-Based Data Management (OBDM), an abstraction of a source query q is a query over the ontology capturing the semantics of q in terms of the concepts and the relations available in the ontology. Since a perfect characterization of a source query may not exist, the notions of best sound and complete approximations of an abstraction have been introduced and studied in the typical OBDM context, i.e., in the case where the ontology is expressed in DL-Lite, and source queries are expressed as unions of conjunctive queries (UCQs). Interestingly, if we restrict our attention to abstractions expressed as UCQs, even best approximations of abstractions are not guaranteed to exist. Thus, a natural question to ask is whether such limitations affect even larger classes of queries. In this paper, we answer this fundamental question for an essential class of queries, namely the class of monotone queries. We define a monotone query language based on disjunctive Datalog enriched with an epistemic operator, and show that its expressive power suffices for expressing the best approximations of monotone abstractions of UCQs.

Topics: AAAI

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HOW TO CITE:

Gianluca Cima||Marco Console||Maurizio Lenzerini||Antonella Poggi Monotone Abstractions in Ontology-Based Data Management Proceedings of the AAAI Conference on Artificial Intelligence (2022) 5556-5563.

Gianluca Cima||Marco Console||Maurizio Lenzerini||Antonella Poggi Monotone Abstractions in Ontology-Based Data Management AAAI 2022, 5556-5563.

Gianluca Cima||Marco Console||Maurizio Lenzerini||Antonella Poggi (2022). Monotone Abstractions in Ontology-Based Data Management. Proceedings of the AAAI Conference on Artificial Intelligence, 5556-5563.

Gianluca Cima||Marco Console||Maurizio Lenzerini||Antonella Poggi. Monotone Abstractions in Ontology-Based Data Management. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.5556-5563.

Gianluca Cima||Marco Console||Maurizio Lenzerini||Antonella Poggi. 2022. Monotone Abstractions in Ontology-Based Data Management. "Proceedings of the AAAI Conference on Artificial Intelligence". 5556-5563.

Gianluca Cima||Marco Console||Maurizio Lenzerini||Antonella Poggi. (2022) "Monotone Abstractions in Ontology-Based Data Management", Proceedings of the AAAI Conference on Artificial Intelligence, p.5556-5563

Gianluca Cima||Marco Console||Maurizio Lenzerini||Antonella Poggi, "Monotone Abstractions in Ontology-Based Data Management", AAAI, p.5556-5563, 2022.

Gianluca Cima||Marco Console||Maurizio Lenzerini||Antonella Poggi. "Monotone Abstractions in Ontology-Based Data Management". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.5556-5563.

Gianluca Cima||Marco Console||Maurizio Lenzerini||Antonella Poggi. "Monotone Abstractions in Ontology-Based Data Management". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 5556-5563.

Gianluca Cima||Marco Console||Maurizio Lenzerini||Antonella Poggi. Monotone Abstractions in Ontology-Based Data Management. AAAI[Internet]. 2022[cited 2023]; 5556-5563.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
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101, Palo Alto, California 94303 All Rights Reserved

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