Contextual proximity based term-weighting for improved web information retrieval
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
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This paper proposes a method to improve the precison of Web retrieval based on proximity and density of keywords for two-keyword queries. In addition, filtering keywords by semantic relationships also be used. We have implemented a system that re-ranks Web search results based on three measures: first-appearance term distance, minimum term distance, and local appearance density. Furthermore, the system enables the user to assign weights to the new rank and original ranks so that the result can be presented in order of the combined rank. We built a prototype user interface in which the user can dynamically change the weights on two different ranks. The result of the experiment showed that our method improves the precision of Web search results for two-keyword queries.