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The paradigm of literature-based knowledge discovery originated by Swanson involves finding meaningful associations between terms or concepts that have not occurred together in any previously published document. While several automated approaches have been applied to this problem, these generally evaluate the literature at a point in time, and do not evaluate the role of change over time in distributional statistics as an indicator of meaningful implicit associations. To address this issue, we develop and evaluate Symmetric Random Indexing (SRI), a novel variant of the Random Indexing (RI) approach that is able to measure implicit association over time. SRI is found to compare favorably to existing RI variants in the prediction of future direct co-occurrence. Summary statistics over several experiments suggest a trend of converging semantic distance prior to the co-occurrence of key terms for two seminal historical literature-based discoveries.
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