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Abstract

This paper describes the development and testing of a novel Automatic Search Query Enhancement (ASQE) algorithm, the Wikipedia N Sub-state Algorithm (WNSSA), which utilises Wikipedia as the sole data source for prior knowledge. This algorithm is built upon the concept of iterative states and sub-states, harnessing the power of Wikipedia's data set and link information to identify and utilise reoccurring terms to aid term selection and weighting during enhancement. This algorithm is designed to prevent query drift by making callbacks to the user's original search intent by persisting the original query between internal states with additional selected enhancement terms. The developed algorithm has shown to improve both short and long queries by providing a better understanding of the query and available data. The proposed algorithm was compared against five existing ASQE algorithms that utilise Wikipedia as the sole data source, showing an average Mean Average Precision (MAP) improvement of 0.273 over the tested existing ASQE algorithms.