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G. Asian and D. McLeod, “Semantic heterogeneity resolution in federated database by metadata implantation and stepwise evolution,” The VLDB Journal, the Interna-tional Journal on Very Large Databases, Vol. 18, pp. 22-31, 1999.

has been cited by the following article:

• TITLE: A Semantic Vector Retrieval Model for Desktop Documents

  AUTHORS: Sheng Li

  KEYWORDS: Semantic Desktop, Information Retrieval, Ontology, Vector Retrieval Model

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.2 No.1, April 16, 2009

  ABSTRACT: The paper provides a semantic vector retrieval model for desktop documents based on the ontology. Comparing with traditional vector space model, the semantic model using semantic and ontology technology to solve several problems that traditional model could not overcome such as the shortcomings of weight computing based on statistical method, the expression of semantic relations between different keywords, the description of document semantic vectors and the similarity calculating, etc. Finally, the experimental results show that the retrieval ability of our new model has significant improvement both on recall and precision.