The problem of top-${kappa}$ set similarity joins finds the top-${kappa}$ pairs of records ranked by their similarities between two sets of input records. We propose an efficient algorithm to return top-${kappa}$ similarity join pairs using a sampling technique. From a sample of the input records, we construct a histogram of set similarity joins, and then compute an estimated similarity threshold in the histogram for top-${kappa}$ join pairs within the error bound of 95% confidence level based on statistical inference. Finally, the estimated threshold is applied to the traditional similarity join algorithm which uses the min-heap structure to get top-${kappa}$ similarity joins. The experimental results show the good performance of the proposed algorithm on large real datasets.