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概率描述逻辑推理的优化与评价:一种系统方法

Optimization and Evaluation of Reasoning in Probabilistic Description Logic: Towards a Systematic Approach
课程网址: http://videolectures.net/iswc08_klinov_oerpdl/  
主讲教师: Pavel Klinov
开课单位: 乌尔姆大学
开课时间: 2008-11-24
课程语种: 英语
中文简介:
本文描述了为概率描述逻辑P $ {\ ensuremath {\ mathcal {SHIQ}}(D)} $开发测试和评估reasoners性能的方法的第一步。由于它是处理DL本体不确定性的新形式,因此没有提出这样的方法。没有足够大的概率本体可用作测试套件。另外,由于P $ {\ ensuremath {\ mathcal {SHIQ}}(D)} $中的推理服务主要是面向查询的,因此没有单一的问题(如经典DL中的分类或实现)可能是一个明显的候选者用于基准测试。所有这些问题使得很难评估reasoners的性能,揭示复杂性瓶颈并评估优化策略的价值。本文通过以下贡献来解决这些重要问题:首先,它描述了为乳腺癌的现实生活领域开发的概率本体论,它对现状提出了重大挑战P $ {\ ensuremath {\ mathcal {SHIQ (D)} $ reasoners。其次,它解释了一种产生一系列概率推理问题的系统方法,这些问题能够评估推理性能,并阐明在实践中推理的原因是什么,在实践中难以实现?$ \ ensuremath {\ mathcal {SHIQ}}(D)} 。最后,本文提出了一种非单调蕴涵的优化算法。使用我们的评估方法证明了其对绩效的积极影响。
课程简介: This paper describes the first steps towards developing a methodology for testing and evaluating the performance of reasoners for the probabilistic description logic P- ${\ensuremath{\mathcal{SHIQ}}(D)}$ . Since it is a new formalism for handling uncertainty in DL ontologies, no such methodology has been proposed. There are no sufficiently large probabilistic ontologies to be used as test suites. In addition, since the reasoning services in P- ${\ensuremath{\mathcal{SHIQ}}(D)}$ are mostly query oriented, there is no single problem (like classification or realization in classical DL) that could be an obvious candidate for benchmarking. All these issues make it hard to evaluate the performance of reasoners, reveal the complexity bottlenecks and assess the value of optimization strategies. This paper addresses these important problems by making the following contributions: First, it describes a probabilistic ontology that has been developed for the real-life domain of breast cancer which poses significant challenges for the state-of-art P- ${\ensuremath{\mathcal{SHIQ}}(D)}$ reasoners. Second, it explains a systematic approach to generating a series of probabilistic reasoning problems that enable evaluation of the reasoning performance and shed light on what makes reasoning in P- ${\ensuremath{\mathcal{SHIQ}}(D)}$ hard in practice. Finally, the paper presents an optimized algorithm for the non-monotonic entailment. Its positive impact on performance is demonstrated using our evaluation methodology.
关 键 词: 概率; 乳腺癌; 优化算法
课程来源: 视频讲座网
最后编审: 2019-04-30:lxf
阅读次数: 45