Web Data Extraction Based on Partial Tree Alignment
Citation
Zhai, Y., Liu, B.: Web data extraction based on partial tree alignment. In: WWW. (2005) 76–85.
Online version
Summary
This paper studies the problem of extracting structured records from semi structured web pages problem which has been recently studied in several researches. Most of the techniques in extracting structured information from the Web are limited by either the following two limitations: they require human labeling of many web pages or they have made many assumptions that are not applicable to many web sites.
This paper present a novel technique which doesn't have the above limitations. The technique has two phases: 1- identifying data fields in the input web page, and 2- extracting data from the identified data fields.
Many of the developed techniques require significant human effort to annotate data for each website or they require a heuristic to be able to extract data types that exist in the webpage. This paper introduces a novel approach to extract semi structured data from web pages by requiring annotating only a few pages for very few websites.
This method first requires a set of web pages which are annotated by human. The annotator should decide what schema columns are interesting are presenting in the input web pages and should also annotates a very small number of web pages for four or six websites. Given this training data, program trains four different classifier (using different types of features) to classify data for each of the annotated fields. Using these trained classifiers, it then tries to extract data that maximize confidence value of trained classifiers.
To evaluate their method they have used regularized logistic regression classifier as the baseline method. The technique is tested on two different vacation rentals and job sites. They have shown that by annotating 2-5 pages for 4-6 web sites their technique can achieve an accuracy of 84% on job offer sites and 91% on vacation rental sites.
