A brief survey of web data extraction tools

TLDR

Data extraction from web pages has attracted recent research, drawing on NLP, machine learning, IR, databases, and ontologies, but differing approaches make direct comparison challenging. The paper proposes a taxonomy, surveys major tools, and offers a qualitative analysis to guide future studies. The authors develop a taxonomy of extraction tools, review existing systems, and qualitatively assess their features.

Abstract

In the last few years, several works in the literature have addressed the problem of data extraction from Web pages. The importance of this problem derives from the fact that, once extracted, the data can be handled in a way similar to instances of a traditional database. The approaches proposed in the literature to address the problem of Web data extraction use techniques borrowed from areas such as natural language processing, languages and grammars, machine learning, information retrieval, databases, and ontologies. As a consequence, they present very distinct features and capabilities which make a direct comparison difficult to be done. In this paper, we propose a taxonomy for characterizing Web data extraction fools, briefly survey major Web data extraction tools described in the literature, and provide a qualitative analysis of them. Hopefully, this work will stimulate other studies aimed at a more comprehensive analysis of data extraction approaches and tools for Web data.