提出首个支持等式约束的多阶依赖目标驱动查询方法，显著提升查询效率。
Goal-Driven Query Answering over First- and Second-Order Dependencies with Equality
- 通过三阶段变换优化依赖关系，聚焦与查询相关的推导路径。
- 实验显示比计算全通用模型快数个数量级。
- 适合处理含函数变量和等式约束的复杂逻辑查询任务。
本文首次提出针对一阶与二阶依赖关系中包含等式的场景，实现目标驱动的查询求解技术。该方法通过一系列步骤对输入依赖进行转换，使得在输出上执行追加（chase）时能避免大量与查询无关的推理。转换过程包含三项创新：第一，提出一种可处理函数变量的奇异化变体，修正了ten Cate等人[73]相关形式化的不完备性；第二，提出相关性分析技术，可消除对查询答案无贡献的依赖；第三，提出一种可处理含等式的二阶依赖的魔集算法变体[19]。我们还进行了广泛的实证评估，结果表明目标驱动的查询回答相比计算完整通用模型可快数个数量级。
原文摘要 · Abstract (English)
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.
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