With the phenomenal growth of the Web, there is an ever-increasing volume of data and information published in numerous Web pages. The research in Web mining aims to develop new techniques to effectively extract and mine useful knowledge/information from these Web pages. Due to the heterogeneity and the lack of structure of Web data, automated discovery of targeted or unexpected knowledge/information is a challenging task. It calls for novel methods that draw from a wide range of fields spanning data mining, machine learning, natural language processing, statistics, databases, and information retrieval. In the past few years, there was already a rapid expansion of activities in the Web mining field, which consists of Web usage mining, Web structure mining, and Web content mining. Web usage mining refers to the discovery of user access patterns from Web usage logs. Web structure mining tries to discover useful knowledge from the structure of hyperlinks. Web content mining aims to extract/mine useful information or knowledge from web page contents.
For this special issue, we focus on Web Content Mining. We invite original and high quality submissions addressing all aspects of Web content mining. Topics that are of interest include but are not limited to:
Submissions (in PDF format only) should be sent to
xxx@cs.uic.edu, and
should not exceed 8 pages. Detailed formatting instructions and templates
are available from:
http://www.acm.org/sigkdd/explorations/instructions.htm.