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Integrating web directories by learning their structures
Conference proceeding

Integrating web directories by learning their structures

Christopher C. Yang and Jianfeng Lin
Proceedings of the 16th international conference on World Wide Web, pp 1239-1240
08 May 2007
DOI: https://doi.org/10.1145/1242572.1242785

Abstract

Information systems -- Information retrieval Information systems -- Information retrieval -- Information retrieval query processing Information systems -- Information retrieval -- Retrieval models and ranking Information systems -- Information retrieval -- Retrieval tasks and goals -- Clustering and classification Information systems -- Information systems applications -- Data mining -- Clustering
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.

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1 citations in Scopus

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