Using concept maps as a cross-language resource discovery tool for large documents in digital libraries

ABSTRACT

Project Gutenburg, the Million Book Project, the Networked Digital Library of Theses and Dissertations, Amazon's book search service, and the recently announced collaboration of Google and leading libraries, all aim to make available large numbers of book-length objects, in a variety of languages. Traditional approaches to discovering a suitable book for a particular purpose have generally relied on catalog records, sometimes enhanced with abstracts. Full-text searching - popular, e.g., with legal and government documents - and passage retrieval techniques, suitable for encyclopedias and reference works, have not been adequately tested with large collections of large objects.

REFERENCES

• 1: Rakesh Agrawal , Ramakrishnan Srikant, Fast Algorithms for Mining Association Rules in Large Databases, Proceedings of the 20th International Conference on Very Large Data Bases, p.487-499, September 12-15, 1994
• 2: Chmielewski, T. L. and Dansereau, D. F. Enhancing the recall of text: Knowledge mapping training promotes implicit transfer. Journal of Educational Psychology, vol. 90(3), pp. 407--413, Sept. 1998.
• 3: Liu, H. "MontyTagger", 1.2 ed. Cambridge, Mass, 2003. http://web.media.mit.edu/~hugo/montytagger/.
• 4: Novak, J. D. and Gowin, D. B. Learning How To Learn. Cambridge, UK, Cambridge University Press, 1984.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Search process

General Terms:
Algorithms, Experimentation, Human Factors

Keywords:
automatic summarization, concept maps, crosslingual information retrieval