Information discovery from semi-structured record sets on the Web

The World Wide Web has been extensively developed since its first appearance two decades ago. Various applications on the Web have unprecedentedly changed humans’ life. Although the explosive growth and spread of the Web have resulted in a huge information repository, yet it is still under-utilized due to the difficulty in automated information extraction (IE) caused by the heterogeneity of Web content. Thus, Web IE is an essential task in the utilization of Web information. Typically, a Web page may describe either a single object or a group of similar objects. For example, the description page of a digital camera describes different aspects of the camera. On the contrary, the faculty list page of a department presents the information of a group of professors. Corresponding to the above two types, Web IE methods can be broadly categorized into two classes, namely, description details oriented extraction and object records oriented extraction. In this book, we focus on the later task, namely semi-structured data record extraction from a single Web page. In this book, we develop two frameworks to tackle the task of data record extraction. We first present a record segmentation search tree framework in which a new search structure, named Record Segmentation Tree (RST), is designed and several efficient search pruning strategies on the RST structure are proposed to identify the records in a given Web page. The subtree groups corresponding to possible data records are dynamically generated in the RST structure during the search process. Therefore, this framework is more flexible compared with existing methods such as MDR and DEPTA that have a static manner of generating subtree groups. Furthermore, instead of using string edit distance or tree edit distance, we propose a token-based edit distance which takes each DOM node as a basic unit in the cost calculation. Many existing methods, including the RST framework, for data record extraction from Web pages contain pre-coded hard criteria and adopt an exhaustive search strategy for