Automatic Taxonomy Extraction Using Google and Term Dependency

An automatic taxonomy extraction algorithm is proposed. Given a set of terms or terminology related to a subject domain, the proposed approach uses Google page count to estimate the dependency links between the terms. A taxonomic link is an asymmetric relation between two concepts. In order to extra...

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Bibliographic Details
Published in:Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence pp. 321 - 325
Main Authors: Makrehchi, Masoud, Kamel, Mohamed S.
Format: Conference Proceeding
Language:English
Published: Washington, DC, USA IEEE Computer Society 02.11.2007
Series:ACM Conferences
Subjects:
Information systems > Information retrieval
Information systems > Information retrieval > Document representation
Information systems > Information retrieval > Evaluation of retrieval results
Information systems > Information retrieval > Search engine architectures and scalability > Search engine indexing
Information systems > Information systems applications > Data mining
Mathematics of computing > Information theory
ISBN:0769530265, 9780769530260
Online Access:Get full text
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Summary:An automatic taxonomy extraction algorithm is proposed. Given a set of terms or terminology related to a subject domain, the proposed approach uses Google page count to estimate the dependency links between the terms. A taxonomic link is an asymmetric relation between two concepts. In order to extract these directed links, neither mutual information nor normalized Google distance can be employed. Using the new measure of information theoretic inclusion index, term dependency matrix, which represents the pair-wise dependencies, is obtained. Next, using a proposed algorithm, the dependency matrix is converted into an adjacency matrix, representing the taxonomy tree. In order to evaluate the performance of the proposed approach, it is applied to several domains for taxonomy extraction.
ISBN:0769530265
9780769530260
DOI:10.1109/WI.2007.26