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Enhancing Magic Sets with an Application to Ontological Reasoning

机译：增强魔术集并应用于本体推理

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Magic sets are a Datalog to Datalog rewriting technique to optimize query answering. The rewritten program focuses on a portion of the stable model(s) of the input program which is sufficient to answer the given query. However, the rewriting may introduce new recursive definitions, which can involve even negation and aggregations, and may slow down program evaluation. This paper enhances the magic set technique by preventing the creation of (new) recursive definitions in the rewritten program. It turns out that the new version of magic sets is closed for Datalog programs with stratified negation and aggregations, which is very convenient to obtain efficient computation of the stable model of the rewritten program. Moreover, the rewritten program is further optimized by the elimination of subsumed rules and by the efficient handling of the cases where binding propagation is lost. The research was stimulated by a challenge on the exploitation of Datalog/DLV for efficient reasoning on large ontologies. All proposed techniques have been hence implemented in the DLV system, and tested for ontological reasoning, confirming their effectiveness.
机译：魔术集是从Datalog到Datalog的重写技术，用于优化查询回答。重写的程序着重于输入程序的稳定模型的一部分，该部分足以回答给定的查询。但是，重写可能会引入新的递归定义，甚至可能涉及否定和聚合，并可能减慢程序评估的速度。本文通过防止在重写程序中创建（新）递归定义来增强魔术设置技术。事实证明，对于具有分层否定和聚合的Datalog程序，魔术集的新版本已关闭，这非常方便，可以有效地计算重写程序的稳定模型。此外，通过消除包含的规则并通过有效处理绑定传播丢失的情况来进一步优化重写的程序。出于对大型本体进行有效推理而利用Datalog / DLV的挑战激发了这项研究。因此，所有提议的技术已在DLV系统中实施，并经过本体论推理测试，证实了其有效性。

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