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Lifting EMMeT to OWL Getting the Most from SKOS

机译:从斯科斯队升起猫头鹰

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SKOS and OWL are quite different but complimentary languages. SKOS is targeted at "cognitive" or "navigational" representations, that is, thesauri, controlled vocabularies, and the like. OWL is targeted at logical representations of conceptual knowledge. To a first approximation, SKOS vocabularies try to capture useful relations between concepts, whereas OWL ontologies aim to capture true relations between concepts. Now, of course, the true is sometimes useful and the useful often true, thus SKOS and OWL overlap to some degree. However, there are applications where we need to know true relations (e.g., generating multiple choice questions). Furthermore, SKOS relations are not precisely specified (by design). For example, many different ways of being useful can be covered by the same SKOS relation, but only one way of being useful is actually applicable to some application. In this paper, we present a case study of modifying a large, existing SKOS vocabulary partially into OWL. This lifting is motivated by an application (generating multiple choice questions) that requires more precision in the representation than SKOS alone supports.
机译:Skos和猫头鹰是完全不同但免费的语言。 Skos是针对的“认知”或“导航”表示,即叙述,受控词汇等。猫头鹰是针对概念知识的逻辑表征。对于第一个近似,Skos词汇表试图捕获概念之间的有用关系,而猫头鹰本体旨在捕捉概念之间的真正关系。现在,当然,真的有时有用,并且通常是真实的,因此斯科斯和猫头鹰重叠到某种程度上。但是,我们需要了解真实关系的应用程序(例如,生成多项选择题)。此外,不准确指定Skos关系(通过设计)。例如,许多有用的方式可以用相同的Skos关系涵盖,但只有一种有用的方式实际上适用于某些应用程序。在本文中,我们展示了一个案例研究,将大型现有的Skos词汇部分分成猫头鹰。这种提升是由应用程序(生成多项选择题)的动机,该应用程序比单独支持的斯科斯在表示中更精确。

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