Web site topic‐hierarchy generation based on link structure

Christopher C. Yang and Nan Liu

Journal of the American Society for Information Science and Technology, 2009, vol. 60, issue 3, 495-508

Abstract: Navigating through hyperlinks within a Web site to look for information from one of its Web pages without the support of a site map can be inefficient and ineffective. Although the content of a Web site is usually organized with an inherent structure like a topic hierarchy, which is a directed tree rooted at a Web site's homepage whose vertices and edges correspond to Web pages and hyperlinks, such a topic hierarchy is not always available to the user. In this work, we studied the problem of automatic generation of Web sites' topic hierarchies. We modeled a Web site's link structure as a weighted directed graph and proposed methods for estimating edge weights based on eight types of features and three learning algorithms, namely decision trees, naïve Bayes classifiers, and logistic regression. Three graph algorithms, namely breadth‐first search, shortest‐path search, and directed minimum‐spanning tree, were adapted to generate the topic hierarchy based on the graph model. We have tested the model and algorithms on real Web sites. It is found that the directed minimum‐spanning tree algorithm with the decision tree as the weight learning algorithm achieves the highest performance with an average accuracy of 91.9%.

Date: 2009
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/asi.20990

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jamist:v:60:y:2009:i:3:p:495-508

Ordering information: This journal article can be ordered from
https://doi.org/10.1002/(ISSN)1532-2890

Access Statistics for this article

More articles in Journal of the American Society for Information Science and Technology from Association for Information Science & Technology
Bibliographic data for series maintained by Wiley Content Delivery ().