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TEAM LinG Comeutational Web Intelligence Intelligent Technology for Web Applications SERIES IN MACHINE PERCEPTION AND ARTIFICIAL INTELLIGENCE* Editors: H. Bunke (Univ. Bern, Switzerland) P. S. P. Wang (Northeastern Univ., USA) Vol. 43: Agent Engineering (Eds. Jiming Liu, Ning Zhong, Yuan Y. Tang and Patrick S. P. Wang) Vol. 44: Multispectral Image Processing and Pattern Recognition (Eds. J. Shen, P. S. P. Wang and T. Zhang) Vol. 45: Hidden Markov Models: Applications in Computer Vision (Eds. H. Bunke and T. Caelli) Vol. 46: Syntactic Pattern Recognition for Seismic Oil Exploration (K. Y. Huang) Vol. 47: Hybrid Methods in Pattern Recognition (Eds. H. Bunke and A. Kandel) Vol. 48: Multimodal Interface for Human-Machine Communications (Eds. P. C. Yuen, Y. Y. Tang and P. S. P. Wang) Vol. 49: Neural Networks and Systolic Array Design (Eds. D. Zhang and S. K. Pal) Vol. 50: Empirical Evaluation Methods in Computer Vision (Eds. H. 1. Christensen and P. J. Phill@s) Vol. 51 : Automatic Diatom Identification (Eds. H. du Buf and M. M. Bayer) Vol. 52: Advances in Image Processing and Understanding A Festschrift for Thomas S. Huwang (Eds. A. C, Bovik, C. W. Chen and D. Goldgof) Vol. 53: Soft Computing Approach to Pattern Recognition and Image Processing (Eds. A. Ghosh and S. K. Pal) Vol. 54: Fundamentals of Robotics - Linking Perception to Action (M. Xie) Vol. 55: Web Document Analysis: Challenges and Opportunities (Eds. A. Antonacopoulos and J. Hu) Vol. 56: Artificial Intelligence Methods in Software Testing (Eds. M. Last, A. Kandel and H. Bunke) Vol. 57: Data Mining in Time Series Databases (Eds. M. Last, A. Kandel and H. Bunke) Vol. 58: Computational Web Intelligence: Intelligent Technology for Web Applications (Eds. Y. Zhang, A. Kandel, T. Y. Lin and Y. Yao) Vol. 59: Fuzzy Neural Network Theory and Application (P. Liu and H. Li) *For the complete list of titles in this series, please write to the Publisher. Series in Machine Perception and Artificial Intelligence - Vol. 58 ComputationalW eb Intelligence Intelligent Technology for Web Applications Editors Y.-Q. Zhang Georgia State University, Atlanta, Georgia, USA A. Kandel Tel-Aviv University, Israel University of South Florida, Tampa, Florida, USA T. Y. Lin San Jose State University, California, USA Y. Y. Yao University of Regina, Canada 1; 43 World Scientific - NEW JERSEY LONDON * SINGAPORE * BElJlNG SHANGHAI * HONG KONG 4 TAIPEI * CHENNAI Published by World Scientific Publishing Co. Re. Ltd. 5 Toh Tuck Link, Singapore 596224 USA ofice: Suite 202,1060 Main Street, River Edge, NJ 07661 UK office: 57 Shelton Street, Covent Garden, London WC2H 9HE British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library. COMPUTATIONALW EB INTELLIGENCE: INTELLIGENT TECHNOLOGY FOR WEB APPLICATIONS Series in Machine Perception and Artificial Intelligence (Vol. 58) Copyright 0 2004 by World Scientific Publishing Co. Re. Ltd. All rights reserved. This book, or parts thereof, may not be reproduced in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system now known or to be invented, without written permissionf rom the Publisher. For photocopying of material in this volume, please pay a copying fee through the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA. In this case permission to photocopy is not required from the publisher. ISBN 981-238-827-3 Printed by FuIsland Offset Printing (S) Pte Ltd, Singapore Preface With explosive growth of data on wired and wireless networks, a significant need exists for a new generation of Web techniques with the ability to intelligently assist users in finding useful Web information and making smart Web decisions. Clearly, the future trend of the Web technology is from the bottom-level data oriented Web to the low-level information oriented Web, then to the middle-level knowledge oriented Web, and finally to the high-level intelligence oriented Web. Thus, it is urgent to develop new intelligent Web techniques for Web applications on wired and wireless networks. Web Intelligence (WI), a new direction for scientific research and development, was introduced at the 24th IEEE Computer Society International Computer Software and Applications Conference in 2000. WI exploits Artificial Intelligence (AI) and advanced Information Technology (IT) on the Web and Internet. In general, AI-based Web techniques can improve Web QoI (Quality of Intelligence). To promote the use of fuzzy Logic in the Internet, Zadeh highlights: “fuzzy logic may replace classical logic as what may be called the brainware of the Internet” at 2001 BISC International Workshop on Fuzzy Logic and the Internet (FLINT2001). So soft computing techniques can play an important role in building the intelligent Web brain. So soft-computing-based Web techniques can enhance Web QoI (Quality of Intelligence). In order to use CI (Computational Intelligence) techniques to make intelligent wired and wireless systems with high QoI, Computational Web Intelligence (CWI) was proposed at the special session on CWI at FUZZ-IEEE’02 of 2002 World Congress on Computational Intelligence. CWI is a hybrid technology of CI and Web Technology (WT) dedicating to increasing QoI of e-Business application systems on the wired and wireless networks. Main CWI techniques V vi Preface include (1) Fuzzy Web Intelligence (FWI), (2) Neural Web Intelligence (NWI), (3) Evolutionary Web Intelligence (EWI), (4) Granular Web Intelligence (GWI), (5) Rough Web Intelligence (RWJ), and (6) Probabilistic Web Intelligence (PWI). Since A1 techniques and CI techniques have different strengths, so the broad question is how to combine the different strengths to make a powerful intelligent Web system. Hybrid Web Intelligence (HWI), a broad hybrid research area, uses AI, CI, BI (Biological Intelligence) and WT to build hybrid intelligent Web systems to serve wired and wireless users effectively and efficiently. For clarity, the first two parts of the book introduce CWI techniques, and the third part presents HWI techniques. Part I (Chapters 1-8) introduces basic methods dealing with Web uncertainty based on FWI, RWI and PWI. In Chapter I, Yager describe a general recommender system framework for e-Business applications. Fuzzy techniques are used to analyze available users’ profiles to make suitable recommendations for the users. In Chapter 2, Nikravesh and Takagi introduce a new intelligent Web search method using the Conceptual Fuzzy Set (CFS). The CFS-based search engine based on GoogleTMi s designed and implemented to generate more human-like search results. In Chapter 3, Berkan and Guner uses fuzzy logic and natural language processing to design a fuzzy question-answer Web system which can find out more satisfactory answers for users. In Chapter 4, Cai, Ye, Pan, Shen and Mark have designed the Content Distribution Networks (CDN) using fuzzy inference to transparently and dynamically redirect user requests to relevant cache servers. Simulation results have indicated that the fuzzy CDN can have higher network utilization and better quality of service. In Chapter 5, Wang presents a fuzzy Web recommendation system for Web users. The dynamic fuzzy method is used to generate fuzzy membership functions and rank candidates online. In Chapter 6, Chen, Chen, Gao, Zhang, Gider, Vuppala and Kraft use the fuzzy linear clustering approach to designing the intelligent search engine that can search for relevant fabrics based on users’ queries. Simulations show that the fuzzy search engine is quite effective. In Chapter 7, Lingras, Yan and Jain propose a new complimentary fuzzy rough clustering method for Web usage mining. The conventional K-means algorithm, a modified K-means algorithm based on rough set theory, and a fuzzy clustering algorithm are compared. In Chapter 8, Butz and Sanscartier present the Web search Preface vii methods using the probabilistic inference with context specific independence and contextual weak independence, respectively. Other traditional Bayesian networks are also discussed for comparison. Part I1 (Chapters 9-13) introduces basic techniques of NWI, EWI and GWI. In Chapter 9, Fong and Hui develop a Web-based expert system using neural networks for convenient vehicle fault diagnosis. Simulation results have shown that the online neural expert system is effective in terms of speed and accuracy. In Chapter 10, Purvis, Harrington and Sembower present a genetic-algorithms-based optimization method to personalize Web documents on Web pages clearly. In Chapter 11, Loia, Senatore and Pedrycz propose a novel P-FCM (Proximity Fuzzy C- Means) to do Web page classification based on a user judgment in term of measure of similarity or dissimilarity among classified Web data. Such a hybrid human-computer Web search engine can simplify Web mining tasks. In Chapter 12, Abraham applies soft computing techniques to design i-Miner that is able to optimize the fuzzy clustering algorithm and analyze Web traffic data. The hybrid Web mining framework using neural networks, fuzzy logic and evolutionary computation is efficient according to simulation results. In Chapter 13, Liu, Wan and Wang propose a Web-based multimedia data retrieval system using the multimedia signal processing method and the content-based audio classification technique. Especially, the emerging audio ontology can be used in Web applications, digital libraries, and others. Part I11 (Chapters 14-25) introduces HWI techniques and their applications. In Chapter 14, Zhou, Qin and Chen develop an effective Chinese Web portal for medical Web information retrieval using meta- search engines, cross-regional search technique, as well as post retrieval analysis technique. Importantly, mutli-language-based Web search techniques are beneficial to different people around the world. In Chapter 15, Chen designs tow new algorithms based on multiplicative query expansion strategies to adaptively improve the query vector. Performance analysis shows that the two new algorithms are much better than two traditional ones. In Chapter 16, Hu and Yo0 apply data mining techniques and information technology to design a novel framework -Bi ological Belationship Extract (BRExtract) to find the protein-protein interaction from large collection of online biomedical biomedical literature. The simulations indicate that the new framework is very effective in mining biological patterns from online biomedical databases. In Chapter 17, Lee proposes a novel iJADE (intelligent Java Agent viii Preface Development Environment) based on intelligent multi-agent system to provide an intelligent agent-based platform for e-commerce applications. Useful functions are also described. In Chapter 18, Fong, Hui and Lee develop a Web content filtering system with low latency and high accuracy. Important potential applications include finding harmful Web materials, and fighting against Web-based terrorism. In Chapter 19, Serag-Eldin, Souafi-Bensafi, Lee, Chan and Nikravesh make a Web- based BICS decision support system using fuzzy searching technology to retrieve approximately relevant results and make relatively satisfactory decisions based on fuzzy decision criteria. Interesting simulation examples are given. In Chapter 20, Efe, Raghavan and Lakhotia introduce a novel link-analysis-based Web search method to improve Web search quality. This new search method is more effective than the keyword-based method in terms of Web search quality. In Chapter 21, Cao, Zhou, Chen, Chan and Lu discuss the mobile agent technology and its applications in electronic commerce, parallel computing, and information retrieval, Web Services and grid computing in widely distributed heterogeneous open networks. In Chapter 22, Panayiotopoulos and Avradinis combine computer graphics technology and Web technology to design intelligent virtual agents on the Web. Web-based intelligent virtual agents have many useful e-Applications. In Chapter 23, Wang introduces a network security technique using data mining techniques. In Chapter 24, Jin, Liu and Wang present a novel peer-to-peer grid model to mobilize distributed resources effectively and optimize global performance of the peer-to-peer grid network. In Chapter 25, Last, Shapira, Elovici, Zaafrany and Kandel propose a new intelligent Web mining based security technique to monitor Web contents. Finally, we would like to express our sincere thanks to all authors for their important contributions. We world like to thank Ian Seldrup and others at World Scientific very much for great help for the final success of this book. This work was partially supported by the National Institute for Systems Test and Productivity at University of South Florida under the USA Space and Naval Warfare Systems Command Grant No. N00039-01- 1-2248 and by the Fulbright Foundation that has granted Prof. Kandel the Fulbright Research Award at Tel-Aviv University, College of Engineering during the academic year 2003-2004. Yan-Qing Zhang, Abraham Kandel, T.Y. Lin, Yiyu Yao May, 2004 Contents Preface ............................................................................................................... v Introduction .................................................................................................... xvii PART I: FUZZY WEB INTELLIGENCE. ROUGH WEB INTELLIGENCE AND PROBABILISTIC WEB INTELLIGENCE 1 . Chapter 1 Recommender Systems Based on Representations .................. 3 1.1 Introduction ..................................................................................... 3 1.2 Recommender Systems .................................................................... 4 1.3 The Representation Schema ............................................................ 5 1.4 Intentionally Expressed Preferences ................................................ 7 1.5 User Profiles .................................................................................. 11 1.6 Using Experience for Justification ................................................ 12 1.7 Conclusion ..................................................................................... 16 Bibliography .................................................................................................... 17 . Chapter 2 Web Intelligence: Concept-Based Web Search ...................... 19 2.1 Introduction ................................................................................... 19 2.2 Fuzzy Conceptual Model and Search Engine ................................ 21 2.3 Construction of RBF network ....................................................... 23 2.4 Generation of CFSs ....................................................................... 24 2.5 Illustrative Example of CFSs ......................................................... 25 2.6 Previous Applications of CFSs ...................................................... 26 2.7 Concept-Based Web Communities for GoogleTM Search Engine . 37 2.8 Challenges and Road Ahead .......................................................... 45 2.9 Conclusions ................................................................................... 47 Bibliography .................................................................................................... 51 ix