Merging of Distributed Topic Maps based on the Subject Identity Measure (SIM) Approach
Merging of Distributed Topic Maps based on the Subject Identity Measure (SIM) Approach
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发表时间：
2004
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通讯作者：
Lutz Maicher;Hans Friedrich Witschel
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作者：
Lutz Maicher;Hans Friedrich Witschel
主题地图“一个主题对应一个主题”的核心理论标准是，如果两个分布式主题地图合并，则会导致严重的问题：根据现有标准，只有当两个主题的主题描述(即所谓的主题标识符或主题定位符)完全相同时，才会合并两个主题。另一方面，从哲学的角度来看，如果两个主题描述了相同的主题，即如果它们意在涉及相同的事物或思想，则应该合并。然而，在分布式环境中，主题地图作者并不总是能够使用共同的词汇表：在这些情况下，即使他们打算描述相同的主题，他们也无法使用相同的主题标识符/定位符。因此，我们提出了基于不同主题特征统计的主题同一性度量(SIM)方法。这种方法一方面与所使用的语言无关，另一方面与这些主题地图中的结构无关。SIM描述了两个分布式主题的主题有多么密切相关，即使作者没有使用共同的词汇表。我们的算法使用尽可能多的信息来支持用户决定合并哪些主题。如果SIM超过给定的阈值，这表明两个主题描述了同一主题，因此建议在过滤过程后合并这些主题。由于主题地图可以转换为RDF和OWL，因此应该加强语义Web应用程序中SIM的重用。
The central theoretical criteria of Topic Maps “One Topic for one Subject” leads to serious problems if two distributed Topic Maps are merged: according to existing standards, two Topics will only be merged if the description of their Subject (i.e. their so-called Subject Identifier or Subject Locator) is exactly identical. On the other hand – from a philosophical point of view – two topics should be merged if they describe the same Subject, i.e. if they are intended to refer to the same thing or idea. In distributed environments, however, Topic Map authors are not always able to use a common vocabulary: in these cases they will fail to use identical Subject Identifiers/Locators even if they intend to describe the same Subject. Therefore, we propose the SIM (Subject Identity Measure) approach which is based on a statistics using different Topic characteristics. This approach is on the one hand independent of the languages used and on the other hand of the structure in these Topic Maps. The SIM describes how closely related the Subjects of two distributed Topics are, even if the authors didn’t use a common vocabulary. Our algorithm uses as much information as possible in order to support users in decisions about which Topics to merge. If the SIM exceeds a given threshold, this indicates that two Topics describe the same Subject and therefore merging of these Topics will be recommended after a filtering process. Because Topic Maps are translatable into RDF and OWL the reuse of the SIM in Semantic Web applications should be enforced.