Magic-Sets for Datalog with Existential Quantifiers

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 MagicSets 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.