Web Data Extraction, Applications and Techniques: A Survey by Emilio Ferrara, Pasquale De Meo, Giacomo Fiumara, Robert Baumgartner.
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.
Comprehensive (> 50 pages) survey of web data extraction. Supplements and updates existing work by its focus on classifying by field of use, web data extraction.
Very likely to lead to adaptation of techniques from one field to another.