Journal of Computer Science

Information Extraction from Hypertext Mark-Up Language Web Pages

Mahmoud Shaker, Hamidah Ibrahim, Aida Mustapha and Lili Nurliyana Abdullah

DOI : 10.3844/jcssp.2009.596.607

Journal of Computer Science

Volume 5, Issue 8

Pages 596-607

Abstract

Problems statement: Nowadays, many users use web search engines to find and gather information. User faces an increasing amount of various HTML information sources. The issue of correlating, integrating and presenting related information to users becomes important. When a user uses a search engine such as Yahoo and Google to seek specific information, the results are not only information about the availability of the desired information, but also information about other pages on which the desired information is mentioned. The number of selected pages is enormous. Therefore, the performance capabilities, the overlap among results for the same queries and limitations of web search engines are an important and large area of research. Extracting information from the web pages also becomes very important because the massive and increasing amount of diverse HTML information sources in the internet that are available to users and the variety of web pages making the process of information extraction from web a challenging problem. Approach: This study proposed an approach for extracting information from HTML web pages which was able to extract relevant information from different web pages based on standard classifications. Results: Proposed approach was evaluated by conducting experiments on a number of web pages from different domains and achieved increment in precision and F measure as well as decrement in recall. Conclusion: Experiments demonstrated that our approach extracted the attributes besides the sub attributes that described the extracted attributes and values of the sub attributes from various web pages. Proposed approach was able to extract the attributes that appear in different names in some of the web pages.

Copyright

© 2009 Mahmoud Shaker, Hamidah Ibrahim, Aida Mustapha and Lili Nurliyana Abdullah. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.