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Existing works for extracting navigation objects from webpages focus onnavigation menus, so as to reveal the information architecture of the site.However, web 2.0 sites such as social networks, e-commerce portals etc. aremaking the understanding of the content structure in a web site increasinglydifficult. Dynamic and personalized elements such as top stories, recommendedlist in a webpage are vital to the understanding of the dynamic nature of web2.0 sites. To better understand the content structure in web 2.0 sites, in thispaper we propose a new extraction method for navigation objects in a webpage.Our method will extract not only the static navigation menus, but also thedynamic and personalized page-specific navigation lists. Since the navigationobjects in a webpage naturally come in blocks, we first cluster hyperlinks intodifferent blocks by exploiting spatial locations of hyperlinks, thehierarchical structure of the DOM-tree and the hyperlink density. Then weidentify navigation objects from those blocks using the SVM classifier withnovel features such as anchor text lengths etc. Experiments on real-world datasets with webpages from various domains and styles verified the effectivenessof our method.

Navigation Objects Extraction for Better Content Structure Understanding