Table 3.

Kullback-Leibler divergence for the path length distributions produced by the simulator and the user study. The table shows the KL divergence from the user study to the ontologies and the optimal and random solutions. The KL divergence measures the number of additional bits required to encode the original distribution, if another distribution is used in its place. The Randomly Generated Ontology column was computed using an average over the three randomly generated ontologies considered. The table shows, that the user study was more similar to the ontologies than to the base lines for the single-target scenario. The results closest to the user study are displayed in bold.

User Study	ICD-10	MeSH	SNOMED CT	Optimal	Randomly Generated Ontology	Random Walk
single-target	0.12	0.08	0.18	0.46	0.97	2.56
multiple-target	1.01	0.74	0.84	1.63	0.55	1.29