Scalable and noise tolerant web knowledge extraction for search task simplification

Abstract

The simplification of key tasks of search engine users by directly returning structured knowledge according to their query intents has attracted much attention from both the industry and the academia. The challenge lies in automatically extracting structured knowledge from noisy and complex web scale websites. Although various automatic wrapper induction algorithms have been proposed, ineffectiveness or inefficiency issues beset many of their web scale applications. In this paper, we propose an unsupervised automatic wrapper induction algorithm, named SKES, to efficiently extract knowledge from semi-structured websites. SKES induces the wrapper in a divide-and-conquer mode; dividing the general wrapper into sub-wrappers that can independently learn from data, making it efficient and easy to implement in a parallel mode. Moreover, by employing techniques such as tag path representation of web pages, SKES can dramatically reduce the number of tags and naturally differentiate their roles. The proposed solution was applied and evaluated on a large number of real websites as well as compared with two existing methods that are most related to it. The proposed method is much more efficient than the existing methods, and provided high extraction accuracy. We have extracted 2.5million entities and 29million data fields from over 10 thousand high traffic websites, which demonstrates the applicability of this method. Furthermore, based on the automatically extracted data, we built a prototype to serve structured knowledge that simplifies the key search tasks of end users. The feedback received for the prototype was highly positive.