elasticsearch significant terms relatedness semantic knowledge graph script

relatedness.json

{
  "size": 0,
  "aggs": {
    "NAME": {
      "terms": {
        "field": "terms"
      },
      "aggs": {
        "terms": {
          "significant_terms": {
            "field": "terms",
            "min_doc_count": 2,
            "script_heuristic": {
      "script": """
        double fgCount = 1.0*params._subset_freq;
        double fgTotal = 1.0*params._subset_size;
        double bgCount = 1.0*params._superset_freq;
        double bgTotal = 1.0*params._superset_size;

        if (fgTotal == 0 || bgTotal == 0) return 0;

        // Compute background probability
        double bgProb = bgCount / bgTotal;

        // Compute expected count in foreground
        double expected = fgTotal * bgProb;

        // Z-score
        double num = fgCount - expected;
        double denom = Math.sqrt(expected * (1.0 - bgProb));
        denom = (denom == 0) ? 1e-10 : denom;
        double z = num / denom;

        // Inlined sigmoid functions
        double s1 = (z + (-80)) / (50 + Math.abs(z + (-80)));
        double s2 = (z + (-30)) / (30 + Math.abs(z + (-30)));
        double s3 = (z + 0) / (30 + Math.abs(z + 0));
        double s4 = (z + 30) / (30 + Math.abs(z + 30));
        double s5 = (z + 80) / (50 + Math.abs(z + 80));

        double result = 0.2 * s1 + 0.2 * s2 + 0.2 * s3 + 0.2 * s4 + 0.2 * s5;

        return Math.round(result * 1e5) / 1e5;
      """
            }
          }
        }
      }
    }
  }
}