TOC: International Journal of Knowledge and Web Intelligence (IJKWI)

Dear Colleagues: It is my pleasure to announce the publication of the latest issue of IJKWI. I invite you to take a moment and review the content of this issue. For further information about the journal and learn how to submit, please see: http://www.inderscience.com/browse/index.php?journalID=284 IJKWI proposes and fosters discussion on the techniques, systems, methods and applications that help the World Wide Web to transform from a static data and information repository into an interactive, dynamic, transparent and secure knowledge and service network. The Web has become a ubiquitous tool for finding and sharing information, and conducting business, learning and entertainment. IJKWI is committed to deepening the understanding of enabling technologies for applying and developing the Web as a global information repository as well as computational, cognitive and social foundations of the Web. Sheryl Brahnam, Lakhmi Jain and Richi Nayak Editors-in-Chief: IJKWI Queensland University of Technology, Brisbane, Australia (r.nayak@qut.edu.au) The contents of the latest issue of: International Journal of Knowledge and Web Intelligence (IJKWI): Volume 1, No 1/2, 2009 Published: Quarterly in Print and Electronically by Inderscience Publishers, UK ISSN (Online): 1755-8263 - ISSN (Print): 1755-8255 http://www.inderscience.com/browse/index.php?journalID=284&year=2009&vol=1&issue=1/2 Paper 1 A fuzzy bi-clustering approach to correlate web users and pages Vassiliki A. Koutsonikola, Athena I. Vakali International Journal of Knowledge and Web Intelligence, Vol. 1, No. 1/2 (2009) pp. 3 - 23 With the rapid development of information technology, the significance of clustering in the process of delivering information to users is becoming more eminent. Especially in the web information space, clustering analysis can prove particularly beneficial for a variety of applications such as web personalisation and profiling, caching and prefetching and content delivery networks. In this paper, we propose a bi-clustering approach, which identifies groups of related web users and pages. The proposed approach is a three-step process that relies on the principles of spectral clustering analysis and provides a fuzzy relation scheme for the revealed users' and pages' clusters. Experiments have been conducted on both synthetic and real datasets to prove the proposed method's efficiency and reveal hidden knowledge. Paper 2 A semantic self-organising webpage-ranking algorithm using computational geometry across different knowledge domains Marios Poulos, Sozon Papavlasopoulos, V.S. Belesiotis, Nikolaos Korfiatis International Journal of Knowledge and Web Intelligence, Vol. 1, No. 1/2 (2009) pp. 24 - 47 In this paper we introduce a method for Web page-ranking, based on computational geometry to evaluate and test by examples, order relationships among web pages belonging to different knowledge domains. The goal is, through an organising procedure, to learn from these examples a real-valued ranking function that induces ranking via a convexity feature. We consider the problem of self-organising learning from numerical data to be represented by a well-fitted convex polygon procedure, in which the vertices correspond to descriptors representing domains of web pages. Results and statistical evaluation of procedure show that the proposed method may be characterised as accurate. Paper 3 An integrated model for next page access prediction F. Khalil, J. Li, H. Wang International Journal of Knowledge and Web Intelligence, Vol. 1, No. 1/2 (2009) pp. 48 - 80 Accurate next web page prediction benefits many applications, e-business in particular. The most widely used techniques for this purpose are Markov Model, association rules and clustering. However, each of these techniques has its own limitations, especially when it comes to accuracy and space complexity. This paper presents an improved prediction accuracy and state space complexity by using novel approaches that combine clustering, association rules and Markov Models. The three techniques are integrated together to maximise their strengths. The integration model has been shown to achieve better prediction accuracy than individual and other integrated models. Paper 4 Exploiting tree structure of a web page for clustering Bhaskar Biswas, Karan Jain, Vipul Mittal, K.K. Shukla International Journal of Knowledge and Web Intelligence, Vol. 1, No. 1/2 (2009) pp. 81 - 94 An approach to designing a Universal Web Wrapper has been in stages of implementation for over years. An issue associated with this is the automated selection of web pages and thereby extraction of content of interest. We propose an algorithm to cluster pages on the basis of their structure. Due to high amount of similarity in these pages, it is be easier to categorise them and extract any particular section of the page. This algorithm makes use of only the structural factors leading to complexity equivalent to O(log n). Further, the algorithm evaluation illustrates the precision and efficiency of the algorithm. Paper 5 An efficient approach for mining web content sensitivity Cheng Wang, Ying Liu, Liheng Jian, Peng Zhang International Journal of Knowledge and Web Intelligence, Vol. 1, No. 1/2 (2009) pp. 95 - 109 Abnormal remarks on the web, such as violence, threat, superstition, etc., may disturb the social order and public morality (referred as sensitive content). To provide a quantitative measure of the sensitivity of a webpage, we propose the concept of web content sensitivity which measures how sensitive a page is. We also propose a web content sensitivity mining approach. Our experiment identified a number of sensitive webpages that traditional frequency-based methods failed to find. By varying the sensitive values of the keywords, different sets of high sensitivity keywords were discovered as well as the corresponding webpages. Paper 6 Semantic lifecycles: modelling, application, authoring, mining, and evaluation of meaningful data Felix Modritscher International Journal of Knowledge and Web Intelligence, Vol. 1, No. 1/2 (2009) pp. 110 - 124 The Semantic Web aims at evolving the 'web of data' to an 'intelligent information space' which is responsive to both human beings and computer systems. Consequently, semantic technologies have emerged in many application fields, primarily to provide intelligent IT systems. Semantics, therefore, is considered to be the essence of systemic intelligence. In this paper, we introduce a lifecycle model for semantics, consisting of five phases: a) modeling; b) application; c) authoring; d) mining; e) evaluation of semantic information. With respect to this model, we analyse semantic lifecycles from former research work and summarise experiences as well as future research activities. Paper 7 Mapping OWL to the Entity Relationship and Extended Entity Relationship models Sikha Bagui International Journal of Knowledge and Web Intelligence, Vol. 1, No. 1/2 (2009) pp. 125 - 149 This paper presents mapping rules to conceptually model an Entity Relationship (ER) diagram and Extended Entity Relationship (EER) diagram from OWL by identifying ER and EER constructs in OWL. OWL has been designed for the semantic web, but data in OWL format is not easy to manipulate or query. The conceptual view of OWL presented in this paper is necessary to understand OWL and OWL data, and will be used to eventually map OWL data to the relational model. Once in the relational model, OWL data can avail of mature relational database technology. For full copies of the above articles, check for this issue of the International Journal of Knowledge and Web Intelligence (IJKWI) in your institution's library. If your library is not currently subscribed to this journal, please recommend an IJKWI subscription to your librarian. CALL FOR PAPERS About The Journal The International Journal of Knowledge and Web Intelligence proposes and fosters discussion on the techniques, systems, methods and applications that help the World Wide Web to transform from a static data and information repository into an interactive, dynamic, transparent and secure knowledge and service network. The Web has become a ubiquitous tool for finding and sharing information, and conducting business, learning and entertainment. This journal is committed to deepening the understanding of enabling technologies for applying and developing the Web as a global information repository as well as computational, cognitive and social foundations of the Web. The journal publishes original papers, review papers, technical reports, survey papers, case studies, conference reports, book reviews, notes, commentaries, and issues. Special issues devoted to important topics in Knowledge and Web Intelligence will occasionally be published. Topics of interest to IJKWI include, but not limited to: * Intelligently searching and navigating the Web * Personalized access to the Web and recommender systems * XML data mining, querying and management * Ontology generation, learning and reasoning * Semantic Web and Web 2.0 * Web Usage, Content and Structure Mining * Web-based decision support systems * Intelligent human-Web interaction * Social network analysis and Web blog mining * Innovations, applications and issues in the domain of intelligent technologies (such as Artificial Intelligence, Data Mining and Machine Learning) for the Web. Submission Procedure The manuscript, in English and not to exceed 20 pages in IJKWI format, must be in Microsoft-Word or PDF file. Special cases exceeding page limit will be considered. Please submit your paper to Dr. Richi Nayak (r.nayak@qut.edu.au) or Prof. Lakhmi Jain (Lakhmi.Jain@unisa.edu.au) as an e-mail attachment. The authors are advised to suggest names, affiliations, and full contact information including email addresses of at least three knowledgeable individuals who have published in the subject area of the paper. Proposals for the special issue on emerging topics are also invited. Contacts: Dr. Richi Nayak Email: r.nayak@qut.edu.au Home Page: http://sky.fit.qut.edu.au/~nayak Prof. Lakhmi C. Jain Email: Lakhmi.Jain@unisa.edu.au Home Page: http://people.unisa.edu.au/Lakhmi.Jain All inquiries and submissions should be sent to: Dr. Richi Nayak r.nayak@qut.edu.au