Information Extraction from Heterogenous Web Sites Using Additional Search of Related Contents Based on a User's Instantiated Example

Recently, since the growth of the Internet, World Wide Web has become significant infrastructure in various fields such as business, commerce, education and so on. Accordingly, a user has gathered information by using the Internet. However due to the flood of Web pages, it becomes difficult for a user to collect desirable information. Advanced Web search engines may provide solution to some extent, it is still up to a user to summarize or extract meaningful information from such retrieval results. Based on this viewpoints, we addressed a generation method of table-style data from heterogeneous Webpages that reflects a user's intention. However if original pages have less information, our system may not extract sufficient information. To improve this problem, we address a method that searches related page contents automatically. We apply this method to shopping sites and the experimental result shows it improves recall rate.