Abstract
Datalog ∃ is the extension of Datalog allowing existentially quantified variables in rule heads. This language is highly expressive and enables easy and powerful knowledge-modelling, but the presence of existentially quantified variables makes reasoning over Datalog ∃ undecidable in the general case. Restricted classes of Datalog ∃ , such as shy, have been proposed in the literature with the aim of enabling powerful, yet decidable query answering on top of Datalog ∃ programs. However, in order to make such languages attractive it is necessary to guarantee good performance for query answering tasks. This paper works in this direction: improving the performance of query answering on Datalog ∃ . To this end, we design a rewriting method extending the well-known Magic-Sets technique to any Datalog ∃ program. We demonstrate that our rewriting method preserves query equivalence on Datalog ∃ , and can be safely applied to shy programs. We therefore incorporate the Magic-Sets method in DLV ∃ , a system supporting shy. Finally, we carry out an experiment assessing the positive impact of Magic-Sets on DLV ∃ , and the effectiveness of the enhanced DLV ∃ system compared to a number of state-of-the-art systems for ontology-based query answering.
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Alviano, M., Leone, N., Manna, M., Terracina, G., Veltri, P. (2012). Magic-Sets for Datalog with Existential Quantifiers. In: Barceló, P., Pichler, R. (eds) Datalog in Academia and Industry. Datalog 2.0 2012. Lecture Notes in Computer Science, vol 7494. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32925-8_5
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