Article

Web Data Extraction, Applications and Techniques: A Survey

ACM Computing Surveys (Under Review) 07/2012;
Source: arXiv

ABSTRACT Web Data Extraction is an important problem that has been studied by means of
different scientific tools and in a broad range of application domains. Many
approaches to extracting data from the Web have been designed to solve specific
problems and operate in ad-hoc application domains. Other approaches, instead,
heavily reuse techniques and algorithms developed in the field of Information
Extraction.
This survey aims at providing a structured and comprehensive overview of the
research efforts made in the field of Web Data Extraction. The fil rouge of our
work is to provide a classification of existing approaches in terms of the
applications for which they have been employed. This differentiates our work
from other surveys devoted to classify existing approaches on the basis of the
algorithms, techniques and tools they use.
We classified Web Data Extraction approaches into categories and, for each
category, we illustrated the basic techniques along with their main variants.
We grouped existing applications in two main areas: applications at the
Enterprise level and at the Social Web level. Such a classification relies on a
twofold reason: on one hand, Web Data Extraction techniques emerged as a key
tool to perform data analysis in Business and Competitive Intelligence systems
as well as for business process re-engineering. On the other hand, Web Data
Extraction techniques allow for gathering a large amount of structured data
continuously generated and disseminated by Web 2.0, Social Media and Online
Social Network users and this offers unprecedented opportunities of analyzing
human behaviors on a large scale.
We discussed also about the potential of cross-fertilization, i.e., on the
possibility of re-using Web Data Extraction techniques originally designed to
work in a given domain, in other domains.

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