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iWarded: A Versatile Generator to Benchmark Warded Datalog+/– Reasoning

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Rules and Reasoning (RuleML+RR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13752))

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Abstract

Warded Datalog+/– is a powerful member of the Datalog+/– family, which extends the logic language Datalog with existential quantification and provides full support for recursion. Such expressive power, paired with a promising trade-off with the offered data complexity, was the catalyst for the recent rise of the language as a relevant candidate for knowledge graph traversal and ontological reasoning applications. Despite the growing research and industrial interest towards Warded Datalog+/–, we observe a substantial lack of specific tools able to generate non-trivial settings and benchmark scenarios, essential to evaluate, analyze and compare reasoning systems over such tasks. In this paper, we aim at filling this gap by introducing iWarded, a versatile generator of Warded Datalog+/– benchmarks. Our system is able to efficiently create very large, complex, and realistic reasoning settings while providing extensive control over the theoretical underpinnings of the language. iWarded was developed and employed in the context of the Vadalog system, a state-of-the-art Warded Datalog+/—based reasoner.

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Acknowledgements

The work on this paper was partially supported by the Vienna Science and Technology Fund (WWTF) grant VRG18-013.

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Authors and Affiliations

  1. Università Roma Tre, Rome, Italy

    Paolo Atzeni & Teodoro Baldazzi

  2. Banca d’Italia, Rome, Italy

    Luigi Bellomarini

  3. TU Wien, Vienna, Austria

    Emanuel Sallinger

  4. University of Oxford, Oxford, UK

    Emanuel Sallinger

Authors
  1. Paolo Atzeni
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  2. Teodoro Baldazzi
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  3. Luigi Bellomarini
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  4. Emanuel Sallinger
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Corresponding author

Correspondence to Teodoro Baldazzi .

Editor information

Editors and Affiliations

  1. Singapore Management University, Singapore, Singapore

    Guido Governatori

  2. Dresden University of Technology, Dresden, Germany

    Anni-Yasmin Turhan

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Atzeni, P., Baldazzi, T., Bellomarini, L., Sallinger, E. (2022). iWarded: A Versatile Generator to Benchmark Warded Datalog+/– Reasoning. In: Governatori, G., Turhan, AY. (eds) Rules and Reasoning. RuleML+RR 2022. Lecture Notes in Computer Science, vol 13752. Springer, Cham. https://doi.org/10.1007/978-3-031-21541-4_8

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  • DOI: https://doi.org/10.1007/978-3-031-21541-4_8

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