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Identifying Semantically Similar Elements in Heterogeneous Spatial Databases Using Predicate Logic Expressions

  • Kristin Stock
  • David Pullar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1580)

Abstract

For data to be successfully integrated, semantically similar database elements must be identified as candidates for merging. However, there may be significant differences between the concepts that participants in the integration exercise hold for the same real world entity. A possible method for identifying semantically similar elements prior to integration is based on cognitive science theory of concept attainment. The theory identifies inclusion rules as being the basis for the highest level of concept attainment, once concepts have been attained at lower, perceptive levels. Predicates can be used to combine inclusion rules as a basis for semantic representation of elements. The predicates for different database elements can then be compared to determine the similarities and differences between the elements. This information can be used to develop a set of semantically similar elements, and then to resolve representational conflicts between the elements prior to integration.

Keywords

Semantic Similarity Conjunctive Normal Form Comparison Ratio Equivalent Element SIGMOD Record 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Kristin Stock
    • 1
  • David Pullar
    • 2
  1. 1.School of Planning, Landscape Architecture and SurveyingQueensland University of TechnologyBrisbane
  2. 2.Department of Geographical Sciences and PlanningUniversity of QueenslandBrisbane

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