Ontological Reasoning over Shy and Warded Datalog$+/-$ for Streaming-based Architectures (technical report)

• URL: http://arxiv.org/abs/2311.12236v1
• Date: Mon, 20 Nov 2023 23:27:43 GMT
• Title: Ontological Reasoning over Shy and Warded Datalog$+/-$ for Streaming-based Architectures (technical report)
• Authors: Teodoro Baldazzi, Luigi Bellomarini, Marco Favorito, Emanuel Sallinger
• Abstract summary: Datalog-based ontological reasoning systems adopt languages, often shared under the collective name of Datalog$ +/-$. In this paper, we focus on two extremely promising, expressive, and tractable languages, namely, Shy and Warded Datalog$ +/-$. We leverage their theoretical underpinnings to introduce novel reasoning techniques, technically, "chase variants", that are particularly fit for efficient reasoning in streaming-based architectures. We then implement them in Vadalog, our reference streaming-based engine, to efficiently solve ontological reasoning tasks over real-world settings.
• Score: 6.689509223124273
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent years witnessed a rising interest towards Datalog-based ontological reasoning systems, both in academia and industry. These systems adopt languages, often shared under the collective name of Datalog$+/-$, that extend Datalog with the essential feature of existential quantification, while introducing syntactic limitations to sustain reasoning decidability and achieve a good trade-off between expressive power and computational complexity. From an implementation perspective, modern reasoners borrow the vast experience of the database community in developing streaming-based data processing systems, such as volcano-iterator architectures, that sustain a limited memory footprint and good scalability. In this paper, we focus on two extremely promising, expressive, and tractable languages, namely, Shy and Warded Datalog$+/-$. We leverage their theoretical underpinnings to introduce novel reasoning techniques, technically, "chase variants", that are particularly fit for efficient reasoning in streaming-based architectures. We then implement them in Vadalog, our reference streaming-based engine, to efficiently solve ontological reasoning tasks over real-world settings.

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