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Bottom-Up Extraction and Trust-Based Refinement of Ontology Metadata

机译:自底向上提取和基于信任的本体元数据优化

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We present a way of building ontologies that proceeds in a bottom-up fashion, defining concepts as clusters of concrete XML objects. Our rough bottom-up ontologies are based on simple relations like association and inheritance, as well as on value restrictions, and can be used to enrich and update existing upper ontologies. Then, we show how automatically generated assertions based on our bottom-up ontologies can be associated with a flexible degree of trust by nonintrusively collecting user feedback in the form of implicit and explicit votes. Dynamic trust-based views on assertions automatically filter out imprecisions and substantially improve metadata quality in the long run
机译:我们提出了一种以自下而上的方式进行构建的本体的方法,将概念定义为具体XML对象的集群。我们的粗略的自下而上的本体基于诸如关联和继承之类的简单关系以及价值限制,可用于丰富和更新现有的上本体。然后,我们展示了如何通过以隐式和显式投票的形式非侵入式收集用户反馈,将基于自下而上本体的自动生成的断言与灵活的信任度相关联。基于断言的基于动态信任的视图可以自动过滤掉断定并从长远来看大大提高了元数据质量

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