Unsupervised wrapper induction using linked data
Unsupervised wrapper induction using linked data
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DOI：
10.1145/2479832.2479845
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发表时间：
2013-06
期刊：
影响因子：
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通讯作者：
Anna Lisa Gentile;Ziqi Zhang;Isabelle Augenstein;F. Ciravegna
中科院分区：
文献类型：
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作者：
Anna Lisa Gentile;Ziqi Zhang;Isabelle Augenstein;F. Ciravegna
这项工作探索了关联数据在Web规模信息提取中的使用，并在包装器归纳任务上显示了令人鼓舞的结果。我们提出了一种简单的基于知识的方法，它(i)在不同的领域具有高度的灵活性，（ii）不需要任何培训材料，而是利用关联数据作为背景知识来源来构建必要的学习资源。这项工作的主要贡献是研究如何使用关联数据——一种不精确、冗余和大规模的知识资源——以有效和高效的方式支持网络规模的信息提取，并确定所涉及的挑战。我们表明，对于所涵盖的领域，关联数据作为信息提取的强大知识资源。在一个公开可用的数据集上的实验表明，在某些条件下，这种简单的无监督方法可以与一些总是依赖于训练数据的复杂技术相比，获得有竞争力的结果。
This work explores the usage of Linked Data for Web scale Information Extraction and shows encouraging results on the task of Wrapper Induction. We propose a simple knowledge based method which is (i) highly flexible with respect to different domains and (ii) does not require any training material, but exploits Linked Data as background knowledge source to build essential learning resources. The major contribution of this work is a study of how Linked Data - an imprecise, redundant and large-scale knowledge resource - can be used to support Web scale Information Extraction in an effective and efficient way and identify the challenges involved. We show that, for domains that are covered, Linked Data serve as a powerful knowledge resource for Information Extraction. Experiments on a publicly available dataset demonstrate that, under certain conditions, this simple unsupervised approach can achieve competitive results against some complex state of the art that always depends on training data.