Genetic Mining of HTML Structures for Effective Web-Document Retrieval

Web-documents have a number of tags indicating the structure of texts. Text segments marked by HTML tags have specific meaning which can be utilized to improve the performance of document retrieval systems. In this paper, we present a machine learning approach to mine the structure of HTML documents...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Vol. 18; no. 3; pp. 243 - 256
Main Authors: Kim, Sun, Zhang, Byoung-Tak
Format: Journal Article
Language:English
Published: Boston Springer Nature B.V 01.05.2003
Subjects:
Algorithms
Artificial intelligence
Computer applications
Data mining
Documents
Genetic algorithms
HTML
HyperText Markup Language
Information retrieval
Machine learning
Mining
Performance enhancement
Retrieval
Studies
Tags
Texts
ISSN:0924-669X, 1573-7497
Online Access:Get full text
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Summary:Web-documents have a number of tags indicating the structure of texts. Text segments marked by HTML tags have specific meaning which can be utilized to improve the performance of document retrieval systems. In this paper, we present a machine learning approach to mine the structure of HTML documents for effective Web-document retrieval. A genetic algorithm is described that learns the importance factors of HTML tags which are used to re-rank the documents retrieved by standard weighting schemes. The proposed method has been evaluated on artificial text sets and a large-scale TREC document collection. Experimental evidence supports that the tag weights are well trained by the proposed algorithm in accordance with the importance factors for retrieval, and indicates that the proposed approach significantly improves the performance in retrieval accuracy. In particular, the use of the document-structure mining approach tends to move relevant documents to upper ranks, which is especially important in interactive Web-information retrieval environments.
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ISSN:0924-669X
1573-7497
DOI:10.1023/A:1023293820057