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What Makes a Good Ontology? A Case-Study in Fine-Grained Knowledge Reuse

机译:什么是好的本体?细粒度知识重用的案例研究

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Understanding which ontology characteristics can predict a "good" quality ontology, is a core and ongoing task in the Semantic Web. In this paper, we provide our findings on which structural ontology characteristics are usually observed in high-quality ontologies. We obtain these findings through a task-based evaluation, where the task is the assessment of the correctness of semantic relations. This task is of increasing importance for a set of novel Semantic Web tools, which perform fine-grained knowledge reuse (i.e., they reuse only appropriate parts of a given ontology instead of the entire ontology). We conclude that, while structural ontology characteristics do not provide statistically significant information to ensure that an ontology is reliable ("good"), in general, richly populated ontologies, with higher depth and breadth variance are more likely to provide reliable semantic content.
机译:理解哪些本体特征可以预测“良好”的质量本体,是语义网中的核心和持续的任务。在本文中,我们提供了有关在高质量本体中通常观察到哪些结构本体特征的发现。我们通过基于任务的评估获得这些发现,其中的任务是评估语义关系的正确性。这项任务对于一组新颖的语义Web工具具有越来越重要的意义,这些工具执行细粒度的知识重用(即,它们仅重用给定本体的适当部分,而不是整个本体)。我们得出的结论是,尽管结构本体特征不提供统计上重要的信息来确保本体是可靠的(“好”),但总的来说,具有较高深度和广度差异的人口稠密的本体更有可能提供可靠的语义内容。

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