Defining logical domains in a web site
HYPERTEXT '00 Proceedings of the eleventh ACM on Hypertext and hypermedia
Reasoning for web document associations and its applications in site map construction
Data & Knowledge Engineering
Analysis of anchor text for web search
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Untangling compound documents on the web
Proceedings of the fourteenth ACM conference on Hypertext and hypermedia
As we may perceive: inferring logical documents from hypertext
Proceedings of the sixteenth ACM conference on Hypertext and hypermedia
A Web-based resource model for scholarship 2.0: object reuse & exchange
Concurrency and Computation: Practice & Experience
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Rapid increase in the number of pages on web sites, and widespread use of search engine optimization techniques, lead to web sites becoming difficult to navigate. Traditional site maps do not provide enough information about the site, and are often outdated. In this paper, we propose a machine learning based algorithm, which, combined with natural language processing, automatically constructs high quality descriptive site maps. In contrast to the previous work, our approach does not rely on heuristic rules to build site maps, and does not require specifying the number of items in a site map in advance. It also generates concise, but descriptive summaries for every site map item. Preliminary experiments with a set of educational web sites show that our method can construct site maps of high quality. An important application of our method is a new paradigm for accessing information on the Web, which integrates searching and browsing.