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Growing Fields of Interest - Using an Expand and Reduce Strategy for Domain Model Extraction

机译:生长兴趣领域 - 使用扩展和减少域模型提取的策略

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Domain hierarchies are widely used as models underlying information retrieval tasks. Formal ontologies and taxonomies enrich such hierarchies further with properties and relationships but require manual effort; therefore they are costly to maintain, and often stale. Folksonomies and vocabularies lack rich category structure. Classification and extraction require the coverage of vocabularies and the alterability of folksonomies and can largely benefit from category relationships and other properties. With Doozer, a program for building conceptual models of information domains, we want to bridge the gap between the vocabularies and Folksonomies on the one side and the rich, expert-designed ontologies and taxonomies on the other. Doozer mines Wikipedia to produce tight domain hierarchies, starting with simple domain descriptions. It also adds relevancy scores for use in automated classification of information. The output model is described as a hierarchy of domain terms that can be used immediately for classifiers and IR systems or as a basis for manual or semi-automatic creation of formal ontologies.
机译:域层次结构被广泛用作底层信息检索任务的模型。正式的本体和分类学,进一步丰富了这些层次结构,而且需要进行手动努力;因此,他们昂贵的维护,经常陈旧。愚蠢的和词汇表缺乏丰富的类别结构。分类和提取需要词语的覆盖和贩毒性的可变地,并且可以在很大程度上受益于类别关系和其他性质。通过Doozer,一个用于建立信息域的概念模型的程序,我们希望在一方面和富人,专业设计的本体和其他小分类物之间弥合词汇和愚蠢的人物之间的差距。 Doozer Mines Wikipedia生成紧密域层次结构,从简单的域名描述开始。它还增加了相关性分数以用于自动分类信息。输出模型被描述为可以立即用于分类器和IR系统的域项的层次结构,或者作为手动或半自动创建正式本体的基础。

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