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Ontology Learning from Online Chinese Encyclopedias

机译:来自在线中文百科全书的本体学习

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An ontology is very important in describing and sharing knowledge of a domain. This paper proposes a method to automatically generate domain ontologies from Chinese encyclopedias on the web. First, we use the terms appears in category systems of encyclopedias as concepts and resolute synonyms, then derive an original taxonomy from Chinese- Wikipedia and Hudong-Baike; for other concepts not in the original taxonomy, we use a set-theory like method to form a directed graph and generate a tree from the graph via maximum-spanning-tree algorithm, and merge the tree into the taxonomy. Then, we use titles of normal articles as instances and populate them via category labels in them. The attributes of concepts and instances are generated from special structures such as InfoBox modules. We learn a plant ontology successfully and the later experiments show that the learnt ontology has well precision and high coverage.
机译:本体在描述和共享域的知识方面非常重要。本文提出了一种自动生成来自Web上的中文百科全书的域本体的方法。首先,我们使用这些术语在百科全书的类别系统中出现,作为概念和oldute同义词,然后从中文 - 维基百科和何新 - 贝克获得原始分类物;对于其他不在原始分类学中的其他概念,我们使用一个像方法,如方法,以通过最大跨越树算法从图中形成一棵指示的图表,并将树与分类物合并到分类中。然后,我们使用正常文章的标题作为实例,并通过它们中的类别标签填充它们。概念和实例的属性是从诸如InfoBox模块的特殊结构生成的。我们成功地学习了植物本体,后来的实验表明,学习本体有很好的精度和高覆盖率。

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