Mining Fuzzy Domain Ontology from Textual Databases
Mining search engine query log is a new method for evaluating web site link structure and information architecture. In this paper we propose a new query-URL co-clustering for a web site useful to evaluate information architecture and link structure. Firstly, all queries and clicked URLs corresponding to particular web site are collected from a query log as bipartite graph, one side for queries and the other side for URLs. Then a new content free clustering is applied to cluster queries and URLs concurrently. Afterwards, based on information entropy, clusters of URLs and queries will be used for evaluating link structure and information architecture respectively. Data sets of different web sites have been extracted from a huge query log to evaluate our method, and experiments show promising result.
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|Item Type:||Conference Paper|
|Keywords:||Fuzzy Domain Ontology, Fuzzy Sets, Semantic Web, Text Mining|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600) > Information Systems Development Methodologies (080608)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Artificial Intelligence and Image Processing not elsewhere classified (080199)
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
|Copyright Owner:||Copyright 2007 IEEE|
|Copyright Statement:||Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Deposited On:||06 Aug 2008 00:00|
|Last Modified:||29 Feb 2012 13:38|
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