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<meta content="Everts, Timothy J." name="eprints.creators_name" />
<meta content="Park, Sung Sik" name="eprints.creators_name" />
<meta content="Kang, Byeong Ho" name="eprints.creators_name" />
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<meta content="2007-02-08" name="eprints.datestamp" />
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Formal Concept Analysis
Web Monitoring
Knowledge Acquisition" name="eprints.keywords" />
<meta content="t is necessary to provide a method to store Web information effectively so it can be utilised as a future knowledge resource. A commonly adopted approach is to classify the retrieved information based on its content. A technique that has been found to be suitable for this purpose is Multiple Classification Ripple-Down Rules (MCRDR). The MCRDR system constructs a classification knowledge base over time using an incremental learning process. This incremental method of acquiring classification knowledge suits the nature of Web information because it is constantly evolving and being updated. However, despite this advantage, the classification knowledge of the MCRDR system is not often utilised for browsing the classified information. This is because it does not directly organise the knowledge in a way that is suitable for browsing. As a result, often an alternate structure is utilised for browsing the information which is usually based on a user's abstract understanding of the information domain. This study investigated the feasibility of utilising the classification knowledge acquired through the use of the MCRDR system as a resource for browsing information retrieved from the WWW. A system was implemented that used the concept lattice-based browsing scheme of Formal Concept Analysis (FCA) to support the browsing of documents based on the MCRDR classification knowledge. The feasibility of utilising classification knowledge as a resource for browsing documents was evaluated statistically. This was achieved by comparing the concept lattice-based browsing approach to a standard one that utilises abstract knowledge of a domain as a resource for browsing the same documents." name="eprints.abstract" />
<meta content="2006-01-16" name="eprints.date" />
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<meta content="Twenty-Ninth Australian Computer Science Conference" name="eprints.event_title" />
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<meta content="16-10 Jan 2006" name="eprints.event_dates" />
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<meta content="1. Becker, P. (2005). "Using intermediate representation systems to interact with concept lattices." Formal Concept Analysis. Third International Conference, ICFCA 2005. Proceedings (Lecture Notes in Artificial Intelligence Vol.3403): 265-268.
2. Boyapati, V., K. Chevrier, et al. (2002). ChangeDetector[tm]: a site-level monitoring tool for the WWW. WWW 2002.
3. Carpineto, C. and G. Romano (2005). Formal Concept Analysis as Mathematical Theory of Concepts and Concept Hierarchies. Formal Concept Analysis Foundations and Applications. B. Ganter, G. Stumme and R. Wille.
4. Cole, R. J. (2000). The Management and Visualisation of Document Collections Using Formal Concept Analysis, Griffith University: 122.
5. Cole, R. J., P. W. Eklund, et al. (2004). Browsing Semi-Structured Texts on the Web Using Formal Concept Analysis. Intelligent Technologies for Information Analysis. N. Zong and J. Liu, Springer: 243-264.
6. Correia, J. H., G. Willie, et al. (2003). "Concpetual Knowledge Discovery - A Human-Centered approach." Applied Artificial Intelligence 17: 281-302.
7. Dazeley, R. and B. Kang (2003). Weighted MCRDR:Deriving Information about Relationships between Classifications in MCRDR. 16th Australian Joint Conference on Artificial Intelligence (AI'03), Perth, Australia.
8. Eklund, P. and B. Wormuth (2005). "Restructuring help systems using formal concept analysis." Formal Concept Analysis. Third International Conference, ICFCA 2005. Proceedings (Lecture Notes in Artificial Intelligence Vol.3403): 129-144.
9. Ganter, B., G. Stumme, et al. (2005). Formal Concept Analysis Foundations and Applications.
10. Garter, B. and R. Willie (1997). "Applied Lattice Theory:Formal Concept Analysis."
11. Kang, B., K. Yoshida, et al. (1997). "Help desk system with intelligent interface." Applied Artificial Intelligence 11(7-8): 611-631.
12. Kim, M. (2003). Document Management and Retrieval for Specialised Domains: An Evolutionary User-Based Approach, University of New South Wales.
13. Kim, M. and P. Compton (2000). Developing a domain-specific Information Retrieval Mechanism. 6th Pacific Knowledge Acquisition Workshop (PKAW 2000), Sydney Australia.
14. Kim, M. and P. Compton (2001). Formal Concept Analysis for Domain-Specific Document Retrieval Systems,. 13th Australian Joint Conference on Artificial Intelligence (AI'01), Adelaide Australia, Springer-Verlag.
15. Kim, M. and P. Compton (2004). "Evolutionary document management and retrieval for specialized domains on the web." International Journal of Human-Computer Studies 60(2): 201 - 241.
16. Kim, Y. S., Byeong H. Kang, et al. (2004). Adaptive Web Document Classification with MCRDR. International Conference on Information Technology: Coding and Computing ITCC 2004, Orleans, Las Vegas, Nevada, USA.
17. Liu, L., C. Pu, et al. (2000). WebCQ: Detecting and Delivering Information Changes on the Web. International Conference on Information and Knowledge Management (CIKM), Washington D.C., ACM Press.
18. Liu, L., W. Tang, et al. (2002). "Information Monitoring on the Web:A Scalable Solution." World Wide Web Journal 5(4).
19. Mladenic, D. (1999). "Text-learning and Related Intelligent Agents." Applications of Intelligent Information Retrieval.
20. Park, S. S., S. K. Kim, Byeong Ho Kang, et al. (2003). Web Information Management System: Personalization and Generalization. the IADIS International Conference WWW/Internet 2003.
21. Park, S. S., Y. S. Kim, Byeong Ho Kanget al. (2004). Web Document Classification: Managing Context Change. IADIS International Conference WWW/Internet 2004, Madrid, Spain.
22. Quan, T. T., S. C. Hui, et al. (2005). "A fuzzy FCA-based approach for citation-based document retrieval." 2004 IEEE Conference on Cybernetics and Intelligent Systems (IEEE Cat.04EX912): 578-83 vol.1.
23. Rajapakse, R. K. and M. Denham (2003). A Reinforcement Strategy for (Formal) Concept and Keyword Weight Learning for Adaptive Information Retrieval. MLIRUM'03: Second Workshop on Machine Learning, Information Retrieval and User Modeling at the Ninth International Conference on User Modeling, Pittsburgh, PA, USA.
24. Richards, D. (1998). The Reuse in Ripple Down Rules Knowledge Based Systems, University of New South Wales.
25. Richards, D. (2001). "Combining cases and rules to provide contextualised knowledge based systems." Modeling and Using Context. Third International and Interdisciplinary Conference, CONTEXT 2001. Proceedings (Lecture Notes in Artificial Intelligence Vol.2116): 465-469.
26. Sebastiani, F. (2002). "Machine learning in automated text categorization." ACM Computing Surveys 34(1): 1-47.
27. Tang, W., L. Liu, et al. (2000). WebCQ Detecting and Delivering Information Changes on the Web. Proc. Int. Conf. on Information and Knowledge Management (CIKM)." name="eprints.referencetext" />
<meta content="Everts, Timothy J. and Park, Sung Sik and Kang, Byeong Ho (2006) Using Formal Concept Analysis with an Incremental Knowledge Acquisition System for Web Document Management. In: Twenty-Ninth Australian Computer Science Conference, 16-10 Jan 2006, Hobart, Australia." name="eprints.citation" />
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<meta content="Kang, Byeong Ho" name="DC.creator" />
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<meta content="t is necessary to provide a method to store Web information effectively so it can be utilised as a future knowledge resource. A commonly adopted approach is to classify the retrieved information based on its content. A technique that has been found to be suitable for this purpose is Multiple Classification Ripple-Down Rules (MCRDR). The MCRDR system constructs a classification knowledge base over time using an incremental learning process. This incremental method of acquiring classification knowledge suits the nature of Web information because it is constantly evolving and being updated. However, despite this advantage, the classification knowledge of the MCRDR system is not often utilised for browsing the classified information. This is because it does not directly organise the knowledge in a way that is suitable for browsing. As a result, often an alternate structure is utilised for browsing the information which is usually based on a user's abstract understanding of the information domain. This study investigated the feasibility of utilising the classification knowledge acquired through the use of the MCRDR system as a resource for browsing information retrieved from the WWW. A system was implemented that used the concept lattice-based browsing scheme of Formal Concept Analysis (FCA) to support the browsing of documents based on the MCRDR classification knowledge. The feasibility of utilising classification knowledge as a resource for browsing documents was evaluated statistically. This was achieved by comparing the concept lattice-based browsing approach to a standard one that utilises abstract knowledge of a domain as a resource for browsing the same documents." name="DC.description" />
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<h1 class="ep_tm_pagetitle">Using Formal Concept Analysis with an Incremental Knowledge Acquisition System for Web Document Management</h1>
<p style="margin-bottom: 1em" class="not_ep_block"><span class="person_name">Everts, Timothy J.</span> and <span class="person_name">Park, Sung Sik</span> and <span class="person_name">Kang, Byeong Ho</span> (2006) <xhtml:em>Using Formal Concept Analysis with an Incremental Knowledge Acquisition System for Web Document Management.</xhtml:em> In: Twenty-Ninth Australian Computer Science Conference, 16-10 Jan 2006, Hobart, Australia.</p><p style="margin-bottom: 1em" class="not_ep_block"></p><table style="margin-bottom: 1em" class="not_ep_block"><tr><td valign="top" style="text-align:center"><a onmouseover="EPJS_ShowPreview( event, 'doc_preview_735' );" href="http://eprints.utas.edu.au/725/1/ACSC2006-Using_FC_with_KA.pdf" onmouseout="EPJS_HidePreview( event, 'doc_preview_735' );"><img alt="[img]" src="http://eprints.utas.edu.au/style/images/fileicons/application_pdf.png" class="ep_doc_icon" border="0" /></a><div class="ep_preview" id="doc_preview_735"><table><tr><td><img alt="" src="http://eprints.utas.edu.au/725/thumbnails/1/preview.png" class="ep_preview_image" border="0" /><div class="ep_preview_title">Preview</div></td></tr></table></div></td><td valign="top"><a href="http://eprints.utas.edu.au/725/1/ACSC2006-Using_FC_with_KA.pdf"><span class="ep_document_citation">PDF</span></a> - Requires a PDF viewer<br />246Kb</td></tr></table><div class="not_ep_block"><h2>Abstract</h2><p style="padding-bottom: 16px; text-align: left; margin: 1em auto 0em auto">t is necessary to provide a method to store Web information effectively so it can be utilised as a future knowledge resource. A commonly adopted approach is to classify the retrieved information based on its content. A technique that has been found to be suitable for this purpose is Multiple Classification Ripple-Down Rules (MCRDR). The MCRDR system constructs a classification knowledge base over time using an incremental learning process. This incremental method of acquiring classification knowledge suits the nature of Web information because it is constantly evolving and being updated. However, despite this advantage, the classification knowledge of the MCRDR system is not often utilised for browsing the classified information. This is because it does not directly organise the knowledge in a way that is suitable for browsing. As a result, often an alternate structure is utilised for browsing the information which is usually based on a user's abstract understanding of the information domain. This study investigated the feasibility of utilising the classification knowledge acquired through the use of the MCRDR system as a resource for browsing information retrieved from the WWW. A system was implemented that used the concept lattice-based browsing scheme of Formal Concept Analysis (FCA) to support the browsing of documents based on the MCRDR classification knowledge. The feasibility of utilising classification knowledge as a resource for browsing documents was evaluated statistically. This was achieved by comparing the concept lattice-based browsing approach to a standard one that utilises abstract knowledge of a domain as a resource for browsing the same documents.</p></div><table style="margin-bottom: 1em" cellpadding="3" class="not_ep_block" border="0"><tr><th valign="top" class="ep_row">Item Type:</th><td valign="top" class="ep_row">Conference or Workshop Item (Paper)</td></tr><tr><th valign="top" class="ep_row">Keywords:</th><td valign="top" class="ep_row">Multiple classification Ripple Down Rules
Formal Concept Analysis
Web Monitoring
Knowledge Acquisition</td></tr><tr><th valign="top" class="ep_row">Subjects:</th><td valign="top" class="ep_row"><a href="http://eprints.utas.edu.au/view/subjects/280201.html">280000 Information, Computing and Communication Sciences > 280200 Artificial Intelligence and Signal and Image Processing > 280201 Expert Systems</a><br /><a href="http://eprints.utas.edu.au/view/subjects/280213.html">280000 Information, Computing and Communication Sciences > 280200 Artificial Intelligence and Signal and Image Processing > 280213 Other Artificial Intelligence</a><br /><a href="http://eprints.utas.edu.au/view/subjects/280205.html">280000 Information, Computing and Communication Sciences > 280200 Artificial Intelligence and Signal and Image Processing > 280205 Text Processing</a></td></tr><tr><th valign="top" class="ep_row">ID Code:</th><td valign="top" class="ep_row">725</td></tr><tr><th valign="top" class="ep_row">Deposited By:</th><td valign="top" class="ep_row"><span class="ep_name_citation"><span class="person_name">Dr Byeong Ho Kang</span></span></td></tr><tr><th valign="top" class="ep_row">Deposited On:</th><td valign="top" class="ep_row">08 Feb 2007</td></tr><tr><th valign="top" class="ep_row">Last Modified:</th><td valign="top" class="ep_row">09 Jan 2008 02:30</td></tr><tr><th valign="top" class="ep_row">ePrint Statistics:</th><td valign="top" class="ep_row"><a target="ePrintStats" href="/es/index.php?action=show_detail_eprint;id=725;">View statistics for this ePrint</a></td></tr></table><p align="right">Repository Staff Only: <a href="http://eprints.utas.edu.au/cgi/users/home?screen=EPrint::View&eprintid=725">item control page</a></p>
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