Advertisement

Web Data Integration System: Approach and Case Study

  • Abdolreza Hajmoosaei
  • Sameem Abdul-Kareem
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 7)

Abstract

There are a lot of valuable data on the web that organizations or users can use to improve their decision making process. It is therefore, very important and critical that this information be complete, precise and can be acquired on time. Most web sources provide data in semi-structured form on the internet. The extraction and combination of semi-structured data from different sources on the internet often fails because of syntactic and semantic differences. The access, retrieval and utilization of information from the different web data sources imposes a need for the data to be integrated. Integration of web data is a complex process because of the open, dynamic and heterogeneity nature of web data. The solution to this problem is a web data integration system. External information can be extracted from web sources and utilized for users through a web data integration system. In this paper, we first propose an approach and architecture for web data integration system and then develop a prototype of the proposed system for Malaysian universities.

Keywords

Web data source Heterogeneity conflict Web data integration 
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Heflin, J., Hendler, J.: 2000: Semantic interoperability on the web. In Extreme Markup Languages (2000), http://www.cs.umd.edu/projects/plus/SHOE/pubs/extreme2000.pdf
  2. 2.
    Kashyap, V., Sheth, A.: Semantic heterogeneity in global information systems: The role of metadata, context and ontologies. In: Papazoglou, M.P., Schlageter, G. (eds.) Cooperative Information Systems: Current Trends and Directions, pp. 139–178. Academic Press Ltd, London (1998)Google Scholar
  3. 3.
    Fensel, D.: Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce. Springer, Heidelberg (2001)Google Scholar
  4. 4.
    Ram, S., Park, J.: Semantic Conflict Resolution Ontology (SCROL): An Ontology for Detecting and Resolving Data and Schema-Level Semantic Conflicts. IEEE Transactions on Knowledge and Data Engineering 16(2), 189–202 (2004)CrossRefGoogle Scholar
  5. 5.
    Arens, Y., Ciiee, Y., Knoblock, A.: SIMS: Integrating data from multiple information sources. In: Information science institute, University of Southern California, U.S.A (1992)Google Scholar
  6. 6.
    Goh, C.H., Bressan, S., Madnick, S., Siegel., M.: Context interchange New features and formalisms for the intelligent integration of information. ACM Transaction on Information Systems 17(3), 270–290 (1999)CrossRefGoogle Scholar
  7. 7.
    Beneventano, D., Bergamaschi, S., Guerra, F., Vincini., M.: The MOMIS approach to information integration. In: ICEIS 2001, Proceedings of the 3rd International Conference on Enterprise Information Systems, Portugal (2001)Google Scholar
  8. 8.
    Visser, P.R., Jones, D.M., Beer, M., Bench-Capon, T., Diaz, B., Shave, M.: Resolving ontological heterogeneity in the KRAFT project. In: Bench-Capon, T.J.M., Soda, G., Tjoa, A.M. (eds.) DEXA 1999. LNCS, vol. 1677, pp. 668–677. Springer, Heidelberg (1999)Google Scholar
  9. 9.
    Mena, E., Kashyap, V.: OBSERVER: An Approach for Query Processing in Global Information Systems based on Interoperation across Pre-existing Ontologies (1996)Google Scholar
  10. 10.
    McGuiness, D.L., Fikes, R., Rice, J., Wilder, S.: The chimaera ontology environment. In: McGuiness, D.L., Fikes, R., Rice, J., Wilder, S. (eds.) Seventh National Conference on Artificial Intelligence (AAAI-2000) (2000)Google Scholar
  11. 11.
    Noy, N.F., Musen, M.A.: Anchor-PROMPT: Using Non-Local Context for Semantic Matching. In: Workshop on Ontologies and Information Sharing. IJCAI, Seattle, WA (2001)Google Scholar
  12. 12.
    Ehrig, M., Staab, S.: Efficiency of Ontology Mapping Approaches. In: Institute AIFB, University of Karlsruhe (2001)Google Scholar
  13. 13.
    Madhavan, J., Bernstein, P.A., Rahm, E.: Generic Schema Matching with Cupid. Proc. Of the 27th Conference on Very Large Databases (2001)Google Scholar
  14. 14.
    Doan, A., Madhavan, J., Domingos, P., Halevy, A.: 2002: Learning to Map between Ontologies on the Semantic Web. In: The Eleventh International World Wide Web Conference (WWW 2002), Hawaii, USA (2002)Google Scholar
  15. 15.
    Giunchiglia, F., Shvaiko, P.: Semantic Matching. CEUR-WS 71 (2003)Google Scholar
  16. 16.
    Thanh Le, B., Dieng-Kuntz, R., Gandon, F.: On Ontology Matching Problems: for building a corporate Semantic Web in a multi-communities organization. In: Institute National and Research in Informatic, Sophia Antipolis, France (2004)Google Scholar
  17. 17.
    Winkler, W.E.: The state of record linkage and current research problems. Statistics of Income Division, Internal Revenue Service Publication R99/04 (1999)Google Scholar
  18. 18.
    Cohen, W., Ravikumar, P., Fienberg, S.: A Comparison of String Distance Metrics for Name Matching Tasks. In: IJCAI 2003, workshop on Information Integration on the Web. (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Abdolreza Hajmoosaei
    • 1
  • Sameem Abdul-Kareem
    • 1
  1. 1.Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala lumpurMalaysia

Personalised recommendations

Cite paper

Buy options