In this paper we present the first goal-driven query answering technique for first- and second-order dependencies with equality. Our technique transforms the input dependencies so that applying the chase to the output avoids many inferences that are irrelevant to the query. The transformation proceeds in several steps, which comprise the following three novel techniques. First, we present a variant of the singularisation technique by Marnette [59] that can handle function variables and that corrects an incompleteness of a related formulation by ten Cate et al. [73]. Second, we present a relevance analysis technique that can eliminate dependencies that provably do not contribute to query answers. Third, we present a variant of the magic sets algorithm [19] that can handle second-order dependencies with equality. We also present the results of an extensive empirical evaluation, which show that goal-driven query answering can be orders of magnitude faster than computing the full universal model.

翻译：本文提出首个面向一阶及带等式二阶依赖的目标驱动查询回答技术。该技术对输入依赖进行转换，使得对输出执行追逐过程时能避免大量与查询无关的推理。转换过程包含多个步骤，涉及以下三项创新技术：首先，我们提出Marnette[59]奇异性化技术的变体，该变体可处理函数变量，并纠正了ten Cate等人[73]相关表述的不完备性；其次，提出相关性分析技术，能够消除被证明对查询答案无贡献的依赖；第三，提出魔集算法[19]的变体，可处理带等式的二阶依赖。我们还展示了大量实证评估结果，表明目标驱动查询回答在性能上可比计算全通用模型快多个数量级。