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摘要翻译：
本体的调试是其广泛应用的重要前提，特别是在依赖日常用户创建和维护知识库的领域，如语义Web。最近的方法使用诊断方法来识别不一致或不一致的本体的原因。然而，在大多数调试场景中，这些方法返回许多替代诊断，从而将故障定位的负担强加给用户。本文演示了如何通过执行一系列观察来识别目标诊断，即通过查询一个关于目标本体蕴涵的先知来识别目标诊断。我们利用典型用户错误的先验概率来建立查询选择的信息论概念。我们的评估表明，与短视策略相比，所提出的方法显著减少了所需查询的数量。我们对不同的用户错误概率分布和不同的先验概率进行了实验。我们的测量结果显示了信息论方法在查询选择方面的优势，即使在只有对先验信息的粗略估计的情况下也是如此。
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英文标题：
《Query strategy for sequential ontology debugging》
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作者：
Kostyantyn Shchekotykhin, Gerhard Friedrich, Philipp Fleiss, Patrick
Rodler
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最新提交年份：
2011
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Logic in Computer Science 计算机科学中的逻辑
分类描述：Covers all aspects of logic in computer science, including finite model theory, logics of programs, modal logic, and program verification. Programming language semantics should have Programming Languages as the primary subject area. Roughly includes material in ACM Subject Classes D.2.4, F.3.1, F.4.0, F.4.1, and F.4.2; some material in F.4.3 (formal languages) may also be appropriate here, although Computational Complexity is typically the more appropriate subject area.
涵盖计算机科学中逻辑的所有方面，包括有限模型理论，程序逻辑，模态逻辑和程序验证。程序设计语言语义学应该把程序设计语言作为主要的学科领域。大致包括ACM学科类D.2.4、F.3.1、F.4.0、F.4.1和F.4.2中的材料；F.4.3（形式语言）中的一些材料在这里也可能是合适的，尽管计算复杂性通常是更合适的主题领域。
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一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
Debugging of ontologies is an important prerequisite for their wide-spread application, especially in areas that rely upon everyday users to create and maintain knowledge bases, as in the case of the Semantic Web. Recent approaches use diagnosis methods to identify causes of inconsistent or incoherent ontologies. However, in most debugging scenarios these methods return many alternative diagnoses, thus placing the burden of fault localization on the user. This paper demonstrates how the target diagnosis can be identified by performing a sequence of observations, that is, by querying an oracle about entailments of the target ontology. We exploit a-priori probabilities of typical user errors to formulate information-theoretic concepts for query selection. Our evaluation showed that the proposed method significantly reduces the number of required queries compared to myopic strategies. We experimented with different probability distributions of user errors and different qualities of the a-priori probabilities. Our measurements showed the advantageousness of information-theoretic approach to query selection even in cases where only a rough estimate of the priors is available.
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PDF链接：
https://arxiv.org/pdf/1004.5339
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