Track: Semantic Web
Integrating Web Directories by Learning their Structures
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.