Making large-scale support vector machine learning practical
Advances in kernel methods
Text classification using ESC-based stochastic decision lists
Proceedings of the eighth international conference on Information and knowledge management
Proceedings of the 10th international conference on World Wide Web
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
ICADL'04 Proceedings of the 7th international Conference on Digital Libraries: international collaboration and cross-fertilization
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Documents in the Web are often organized using category trees by information providers (e.g. CNN, BBC) or search engines (e.g. Google, Yahoo!). Such category trees are commonly known as Web directories. The category tree structures from different internet content providers may be similar to some extent but are usually not exactly the same. As a result, it is desirable to integrate these category trees together so that web users only need to browse through a unified category tree to extract information from multiple providers. In this paper, we address this problem by capturing structural information of multiple category trees, which are embedded with the knowledge of professional in organizing the documents. Our experiments with real Web data show that the proposed technique is promising.