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Probabilistic DL Reasoning with Pinpointing Formulas: A Prolog-based Approach

机译：带有精确定位公式的概率DL推理：一种基于Prolog的方法

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摘要

When modeling real-world domains, we have to deal with information that is incomplete or that comes from sources with different trust levels. This motivates the need for managing uncertainty in the Semantic Web. To this purpose, we introduced a probabilistic semantics, named DISPONTE, in order to combine description logics (DLs) with probability theory. The probability of a query can be then computed from the set of its explanations by building a Binary Decision Diagram (BDD). The set of explanations can be found using the tableau algorithm, which has to handle non-determinism. Prolog, with its efficient handling of non-determinism, is suitable for implementing the tableau algorithm. TRILL and TRILLP are systems offering a Prolog implementation of the tableau algorithm. TRILLP builds a pinpointing formula that compactly represents the set of explanations and can be directly translated into a BDD. Both reasoners were shown to outperform state-othe-art DL reasoners. In this paper, we present an improvement of TRILLP, named TORNADO, in which the BDD is directly built during the construction of the tableau, further speeding up the overall inference process. An experimental comparison shows the effectiveness of TORNADO. All systems can be tried online in the TRILL on SWISH web application at http://trill.ml.unife. it/.
机译：在对现实世界域建模时，我们必须处理不完整的信息或来自具有不同信任级别的来源的信息。这激发了在语义网中管理不确定性的需求。为此，我们引入了一种称为DISPONTE的概率语义，以便将描述逻辑（DL）与概率论相结合。然后，可以通过构建二进制决策图（BDD）从其解释集中计算查询的概率。可以使用tableau算法找到这组说明，该算法必须处理非确定性问题。 Prolog具有对非确定性的有效处理能力，适合用于实现tableau算法。 TRILL和TRILLP是提供tableau算法的Prolog实现的系统。 TRILLP建立了一个精确的公式，该公式紧凑地表示了这组解释，可以直接转换为BDD。事实证明，这两种推理机均优于最新的DL推理机。在本文中，我们提出了一种名为TORNADO的TRILLP改进，在该模型的构建过程中直接构建了BDD，从而进一步加快了整个推理过程。实验比较表明TORNADO的有效性。可以在TRILL on SWISH Web应用程序上在线尝试所有系统，网址为http：//trill.ml.unife。它/。

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