ISSN : 1796-217X
Volume : 3    Issue : 8    Date : November 2008

Automatic Discovery of Semantic Relations Based on Association Rule
Xiangfeng Luo, Kai Yan, and Xue Chen
Page(s): 11-18
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Automatic discovery of semantic relations between resources is a key issue in Web-based
intelligent applications such as document understanding and Web services. This paper explores
how to automatically discover the latent semantic relations and their properties based on the
existing association rules. Through building semantic matrix by the association rules, four semantic
relations can be extracted using union and intersection in set theory. By building a cyclic graph
model, the transitive path of association relation is discovered. Document-level keywords and
domain-level keywords as well as their parameters are analyzed to improve the discovery accuracy.
Rules can be gained from the experiments to optimize the discovery processes for relations and
properties. Further experiments validate the effectiveness and efficiency of the relation discovery
algorithms, which can be applied in Web search, intelligent browsing and Web service composition.

Index Terms
Algorithm, Association Rule, Semantic Relation, Transitivity