Inferring Document Similarity from Hyperlinks

Assessing semantic similarity between text documents is a crucial aspect in Information Retrieval systems. In this work, we propose to use hyperlink information to derive a similarity measure that can then be applied to compare any text documents, with or without hyperlinks. As linked documents are generally semantically closer than unlinked documents, we use a training corpus with hyperlinks to infer a function $a,b \to sim(a,b)$ that assigns a higher value to linked documents than to unlinked ones. Two sets of experiments on different corpora show that this function compares favorably with {\em OKAPI} matching on document retrieval tasks.


Published in:
ACM Conference on Information and Knowledge Management
Presented at:
ACM Conference on Information and Knowledge Management
Year:
2005
Publisher:
Bremen, Germany
Keywords:
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 Record created 2006-03-10, last modified 2018-03-17

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