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Query answering DL-lite knowledge bases from hidden datasets

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Abstract

Unifying access to data using structured knowledge is the main problem studied in ontology-based data access (OBDA). Data are often provided by several information sources, and this has led to a number of methods that merge them in order to get a unified point of view. Existing merging approaches assume that the content of datasets is known and available. However, in several applications, it might be impossible to know the full content of datasets beforehand. This paper investigates several query answering strategies from multiple datasets without knowing their content in advance. We study those strategies from different points of view: productivity, logical properties and computational complexity.

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Acknowledgments

This work has received support from the European Project H2020 Marie Sklodowska-Curie Actions (MSCA), Research and Innovation Staff Exchange (RISE): Aniage project (High Dimensional Heterogeneous Data based Animation Techniques for Southeast Asian Intangible Cultural Heritage Digital Content), project number 691215. The third author also received support from the AAP A2U QUID (Querying heterogeneous Data) project.

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Authors and Affiliations

  1. MARS Research Laboratory, University of Sousse, Sousse, Tunisia

    Ghassen Hamdi & Mohamed Nazih Omri

  2. CRIL., University of Artois, Lens, France

    Salem Benferhat & Zied Bouraoui

  3. Aix-Marseille Université, Université de Toulon, LIS, CNRS, Marseille, France

    Odile Papini

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  1. Ghassen Hamdi
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  2. Mohamed Nazih Omri
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  3. Salem Benferhat
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  4. Zied Bouraoui
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  5. Odile Papini
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Correspondence to Ghassen Hamdi.

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Hamdi, G., Omri, M.N., Benferhat, S. et al. Query answering DL-lite knowledge bases from hidden datasets. Ann Math Artif Intell 89, 271–299 (2021). https://doi.org/10.1007/s10472-020-09714-2

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