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Redescription Mining for Learning Definitions and Disjointness Axioms in Linked Open Data

机译:在链接开放数据中的学习定义和脱节公理的重新选择挖掘

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In this article, we present an original use of Redescription Mining (RM) for discovering definitions of classes and incompatibility (disjointness) axioms between classes of individuals in the web of data. RM is aimed at mining alternate descriptions from two datasets related to the same set of individuals. We reuse this process for providing definitions in terms of necessary and sufficient conditions to categories in DBpedia. Firstly, we recall the basics of redescription mining and make precise the principles of our definitional process. Then we detail experiments carried out on datasets extracted from DBpedia. Based on the output of the experiments, we discuss the strengths and the possible extensions of our approach.
机译:在本文中,我们提出了一种原始使用Redescription Mining(RM),用于发现数据网中的个体类之间的类别和不相容性(脱节)公理的定义。 RM旨在挖掘与与同一组数据相关的两个数据集的替代描述。我们重复使用此过程以在DBPedia中必要和充分的条件提供定义。首先,我们记得重新发现挖掘的基础知识,并确切地制定了定义过程的原则。然后我们详细介绍了从DBPedia提取的数据集进行的实验。根据实验的产出,我们讨论了我们方法的优势和可能的延伸。

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