Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Creating a Web community chart for navigating related communities
Proceedings of the 12th ACM conference on Hypertext and Hypermedia
Extracting Large-Scale Knowledge Bases from the Web
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Untangling compound documents on the web
Proceedings of the fourteenth ACM conference on Hypertext and hypermedia
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
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In recent years, many algorithms for the Web have been developed that work with information units distinct from individual web pages. These include segments of web pages or aggregation of web pages into web communities. Using these logical information units has been shown to improve the performance of many web algorithms. In this paper, we focus on a type of logical information units called compound documents. We argue that the ability to identify compound documents can improve information retrieval, automatic metadata generation, and navigation on the Web. We propose a unified framework for identifying the boundaries of compound documents, which combines both structural and content features of constituent web pages. The framework is based on a combination of machine learning and clustering algorithms, with the former algorithm supervising the latter one. Experiments on a collection of educational web sites show that our approach can reliably identify most of the compound documents on these sites.