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Memory-Saving Evaluation Plans for Datalog

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Logics in Artificial Intelligence (JELIA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11468))

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Abstract

Ontology-based query answering (OBQA), without any doubt, represents one of the fundamental reasoning services in Semantic Web applications. Specifically, OBQA is the task of evaluating a (conjunctive) query over a knowledge base (KB) consisting of an extensional dataset paired with an ontology. A number of effective practical approaches proposed in the literature rewrite the query and the ontology into an equivalent Datalog program. In case of very large datasets, however, classical approaches for evaluating such programs tend to be memory consuming, and may even slow down the computation. In this paper, we explain how to compute a memory-saving evaluation plan consisting of an optimal indexing schema for the dataset together with a suitable body-ordering for each Datalog rule. To evaluate the quality of our approach, we compare our plans with the classical approach used by DLV over widely used ontological benchmarks. The results confirm the memory usage can be significantly reduced without paying any cost in efficiency.

This work has been partially supported by Samsung under project “Enhancing the DLV system for large-scale ontology reasoning”, by MISE under project “S2BDW” (F/050389/01-03/X32)-“Horizon2020” PON I&C2014-20, by Regione Calabria under project “DLV LargeScale” (CUP J28C17000220006) - POR Calabria 2014-20, and by the European Union’s Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement No. 690974 for the project “MIREL: MIning and REasoning with Legal texts”.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Calabria, Rende, Italy

    Roberta Costabile, Alessio Fiorentino, Simona Perri & Jessica Zangari

  2. Samsung Research, Staines-upon-Thames, UK

    Carlo Allocca

Authors
  1. Carlo Allocca
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  2. Roberta Costabile
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  3. Alessio Fiorentino
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  4. Simona Perri
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  5. Jessica Zangari
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Corresponding author

Correspondence to Jessica Zangari .

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Editors and Affiliations

  1. University of Calabria, Rende, Italy

    Francesco Calimeri

  2. University of Calabria, Rende, Italy

    Nicola Leone

  3. Mathematics and Computer Science, University of Calabria, Rende, Italy

    Marco Manna

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Allocca, C., Costabile, R., Fiorentino, A., Perri, S., Zangari, J. (2019). Memory-Saving Evaluation Plans for Datalog. In: Calimeri, F., Leone, N., Manna, M. (eds) Logics in Artificial Intelligence. JELIA 2019. Lecture Notes in Computer Science(), vol 11468. Springer, Cham. https://doi.org/10.1007/978-3-030-19570-0_29

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  • DOI: https://doi.org/10.1007/978-3-030-19570-0_29

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  • Print ISBN: 978-3-030-19569-4

  • Online ISBN: 978-3-030-19570-0

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