Unsupervised wrapper induction using linked data

26Citations
Citations of this article
28Readers
Mendeley users who have this article in their library.

Abstract

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 resourcecan 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 un-supervised approach can achieve competitive results against some complex state of the art that always depends on training data. Copyright 2013 ACM.

Cite

CITATION STYLE

APA

Gentile, A. L., Zhang, Z., Augenstein, I., & Ciravegna, F. (2013). Unsupervised wrapper induction using linked data. In Proceedings of the 7th International Conference on Knowledge Capture: “Knowledge Capture in the Age of Massive Web Data”, K-CAP 2013. https://doi.org/10.1145/2479832.2479845

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free