JOURNAL ARTICLE

NaviSOM: Automatic Construction of Navigational Structure for Web Pages using Self-organizing Maps

Hsin-Chang YangChung-Hong Lee

Year: 2006 Journal:   The 2006 IEEE International Joint Conference on Neural Network Proceedings Pages: 985-992

Abstract

One major approach for information finding in the WWW is to navigate through some web directories and browse them for the goal pages. However, such directories are generally constructed manually and have disadvantages of narrow coverage and inconsistency. In this work, we propose NaviSOM, a machine learning approach to automatically construct a navigational structure for the WWW. A self-organizing map is constructed to train the web pages and obtain two feature maps, which reveal the relationships among web pages and thematic keywords respectively. We then use these maps to develop a structure that may assist the users finding the information they need. We used a small set of web pages in the experiments and obtained promising result.

Keywords:
Computer science Web page Construct (python library) Information retrieval Set (abstract data type) World Wide Web Web mapping Thematic map Feature (linguistics) HITS algorithm Web navigation Static web page Geography Cartography

Metrics

1
Cited By
0.00
FWCI (Field Weighted Citation Impact)
20
Refs
0.30
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Web Data Mining and Analysis
Physical Sciences →  Computer Science →  Information Systems
Advanced Image and Video Retrieval Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Text and Document Classification Technologies
Physical Sciences →  Computer Science →  Artificial Intelligence

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