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IJMLC 2014 Vol.4(2): 146-150 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.403

A Robust Framework for Web Information Extraction and Retrieval

Rayner Alfred, Gan Kim Soon, Chin Kim On, and Patricia Anthony

Abstract—The large volume of online and offline information that is available today has overwhelmed users’ efficiency and effectiveness in processing this information in order to extract relevant information. The exponential growth of the volume of Internet information complicates information access. Thus, it is a very time consuming and complex task for user in accessing relevant information. Information retrieval (IR) is a branch of artificial intelligence that tackles the problem of accessing and retrieving relevant information. The aim of IR is to enable the available data source to be queried for relevant information efficiently and effectively. This paper describes a robust information retrieval framework that can be used to retrieve relevant information. The proposed information retrieval framework is designed to assist users in accessing relevant information effectively and efficiently as it handles queries based on user preferences. Each component and module involved in the proposed framework will be explained in terms of functionality and the processes involved.

Index Terms—Information retrieval, information retrieval framework, semantic web.

Rayner Alfred, Gan Kim Soon, and Chin Kim On are with the COESA, Universiti Malaysia Sabah, 88400 Kota Kinabalu, Sabah, Malaysia (e-mail: ralfred@ums.edu.my, g_k_s967@yahoo.com, kimonchin@ums.edu.my).
Patricia Anthony is with the Department of Applied Computing, Faculty of Environment, Society and Design, Lincoln University, Christchurch, New Zealand (e-mail: patricia.anthony@lincoln.ac.nz).


Cite: Rayner Alfred, Gan Kim Soon, Chin Kim On, and Patricia Anthony, "A Robust Framework for Web Information Extraction and Retrieval," International Journal of Machine Learning and Computing vol.4, no. 2, pp. 146-150, 2014.

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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