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Modelling Vague Content and Structure Querying in XML Retrieval with a Probabilistic Object-Relational Framework

机译:使用概率对象关系框架在XML检索中建模模糊的内容和结构查询

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Many XML retrieval applications require relevance-oriented ranking of retrieved elements in order to capture the vagueness inherent to the information retrieval process. This relevance-oriented ranking should not only support vagueness at the content level, but also at the structural level. In this paper, we use a probabilistic object-relational framework to model representation and retrieval strategies that take into account vagueness at both content and structure level. Our approach makes use of established database technology combined with sound probability theory, thus allowing for fast and flexible prototyping of various representation and retrieval strategies.
机译:许多XML检索应用程序要求对检索到的元素进行面向相关性的排名,以便捕获信息检索过程固有的模糊性。这种面向相关性的排名不仅应支持内容级别的模糊性,而且还应支持结构级别的模糊性。在本文中,我们使用概率对象关系框架对表示和检索策略进行建模,该策略考虑了内容和结构级别的模糊性。我们的方法利用成熟的数据库技术和合理的概率论相结合,从而可以快速灵活地对各种表示和检索策略进行原型制作。

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