Classifying Web Pages by Using Knowledge Bases for Entity Retrieval

Authors:
Yusuke Kiritani;Qiang Ma;Masatoshi Yoshikawa
Affiliations:
Department of Social Informatics, Graduate School of Informatices, Kyoto University, Kyoto, Japan 606---8501;Department of Social Informatics, Graduate School of Informatices, Kyoto University, Kyoto, Japan 606---8501;Department of Social Informatics, Graduate School of Informatices, Kyoto University, Kyoto, Japan 606---8501
Venue:
DEXA '09 Proceedings of the 20th International Conference on Database and Expert Systems Applications
Year:
2009

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Abstract

In this paper, we propose a novel method to classify Web pages by using knowledge bases for entity search, which is a kind of typical Web search for information related to a person, location or organization. First, we map a Web page to entities according to the similarities between the page and the entities. Various methods for computing such similarity are applied. For example, we can compute the similarity between a given page and a Wikipedia article describing a certain entity. The frequency of an entity appearing in the page is another factor used in computing the similarity. Second, we construct a directed acyclic graph, named PEC graph, based on the relations among Web pages, entities, and categories, by referring to YAGO, a knowledge base built on Wikipedia and WordNet. Finally, by analyzing the PEC graph, we classify Web pages into categories. The results of some preliminary experiments validate the methods proposed in this paper.