句法与语义的局部性：一个便宜的近似有多好？

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摘要翻译：
提取一个给定的OWL本体的子集来捕获关于指定术语集的所有本体知识是一个很好理解的任务。例如，该任务可以基于基于位置的模块。它们有两种类型，句法的和语义的，句法的LBM包含相应的语义LBM。对于句法LBM，多项式提取算法是已知的，在OWL API中实现，并正在使用。相比之下，语义LBM的提取涉及到推理，这对于OWL2 DL来说是很困难的，而且这些算法还没有在表达型本体语言中实现。我们给出了语义LBMs的第一个实现，并报告了将它们与从现实生活中的本体中提取的句法LBMs进行比较的实验。我们的研究揭示了语义LBM与句法LBM相比是否值得额外的抽取努力。
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英文标题：
《Syntactic vs. Semantic Locality: How Good Is a Cheap Approximation?》
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作者：
Chiara Del Vescovo, Pavel Klinov, Bijan Parsia, Uli Sattler, Thomas
  Schneider, Dmitry Tsarkov
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最新提交年份：
2012
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分类信息：

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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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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英文摘要：
  Extracting a subset of a given OWL ontology that captures all the ontology's knowledge about a specified set of terms is a well-understood task. This task can be based, for instance, on locality-based modules (LBMs). These come in two flavours, syntactic and semantic, and a syntactic LBM is known to contain the corresponding semantic LBM. For syntactic LBMs, polynomial extraction algorithms are known, implemented in the OWL API, and being used. In contrast, extracting semantic LBMs involves reasoning, which is intractable for OWL 2 DL, and these algorithms had not been implemented yet for expressive ontology languages. We present the first implementation of semantic LBMs and report on experiments that compare them with syntactic LBMs extracted from real-life ontologies. Our study reveals whether semantic LBMs are worth the additional extraction effort, compared with syntactic LBMs.
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PDF链接：
https://arxiv.org/pdf/1207.1641

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