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.
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Kiritani, Y., Ma, Q., Yoshikawa, M. (2009). Classifying Web Pages by Using Knowledge Bases for Entity Retrieval. In: Bhowmick, S.S., Küng, J., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2009. Lecture Notes in Computer Science, vol 5690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03573-9_64
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DOI: https://doi.org/10.1007/978-3-642-03573-9_64
Publisher Name: Springer, Berlin, Heidelberg
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