Statistical Web Object Extraction

View Sample PDF

Author(s): Jun Zhu (Tsinghua University, China), Zaiqing Nie (Web Search and Mining Group Microsoft Research Asia, China)and Bo Zhang (Tsinghua University, China)
Copyright: 2009
Pages: 5
Source title: Encyclopedia of Data Warehousing and Mining, Second Edition
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-60566-010-3.ch283

Purchase

View Statistical Web Object Extraction on the publisher's website for pricing and purchasing information.

Abstract

The World Wide Web is a vast and rapidly growing repository of information. There are various kinds of objects, such as products, people, conferences, and so on, embedded in both statically and dynamically generated Web pages. Extracting the information about real-world objects is a key technique for Web mining systems. For example, the object-level search engines, such as Libra (http://libra.msra.cn) and Rexa (http://rexa.info), which help researchers find academic information like papers, conferences and researcher’s personal information, completely rely on structured Web object information. However, how to extract the object information from diverse Web pages is a challenging problem. Traditional methods are mainly template-dependent and thus not scalable to the huge number of Web pages. Furthermore, many methods are based on heuristic rules. So they are not robust enough. Recent developments in statistical machine learning make it possible to develop advanced statistical Web object extraction models. One key difference of Web object extraction from traditional information extraction from natural language text documents is that Web pages have plenty of structure information, such as two-dimensional spatial layouts and hierarchical vision tree representation. Statistical Web object extraction models can effectively leverage this information with properly designed statistical models. Another challenge of Web object extraction is that many text contents on Web pages are not regular natural language sentences. They have some structures but are lack of natural language grammars. Thus, existing natural language processing (NLP) techniques are not directly applicable. Fortunately, statistical Web object extraction models can easily merge with statistical NLP methods which have been the theme in the field of natural language processing during the last decades. Thus, the structure information on Web pages can be leveraged to help process text contents, and traditional NLP methods can be used to extract more features. Finally, the Web object extraction from diverse and large-scale Web pages provides a valuable and challenging problem for machine learning researchers. To nicely solve the problem, new learning methodology and new models (Zhu et al., 2007b) have to be developed.

The IRMA Community

Research IRM

Statistical Web Object Extraction

Purchase

Abstract

Related Content

IRMA Sponsors