  This paper introduces HopRank, an algorithm for modeling human navigation on
semantic networks. HopRank leverages the assumption that users know or can see
the whole structure of the network. Therefore, besides following links, they
also follow nodes at certain distances (i.e., k-hop neighborhoods), and not at
random as suggested by PageRank, which assumes only links are known or visible.
We observe such preference towards k-hop neighborhoods on BioPortal, one of the
leading repositories of biomedical ontologies on the Web. In general, users
navigate within the vicinity of a concept. But they also "jump" to distant
concepts less frequently. We fit our model on 11 ontologies using the
transition matrix of clickstreams, and show that semantic structure can
influence teleportation in PageRank. This suggests that users--to some
extent--utilize knowledge about the underlying structure of ontologies, and
leverage it to reach certain pieces of information. Our results help the
development and improvement of user interfaces for ontology exploration.
