  Similarity networks are important abstractions in many information management
applications such as recommender systems, corpora analysis, and medical
informatics. For instance, by inducing similarity networks between movies rated
similarly by users, or between documents containing common terms, and or
between clinical trials involving the same themes, we can aim to find the
global structure of connectivities underlying the data, and use the network as
a basis to make connections between seemingly disparate entities. In the above
applications, composing similarities between objects of interest finds uses in
serendipitous recommendation, in storytelling, and in clinical diagnosis,
respectively. We present an algorithmic framework for traversing similarity
paths using the notion of `hammock' paths which are generalization of
traditional paths. Our framework is exploratory in nature so that, given
starting and ending objects of interest, it explores candidate objects for path
following, and heuristics to admissibly estimate the potential for paths to
lead to a desired destination. We present three diverse applications: exploring
movie similarities in the Netflix dataset, exploring abstract similarities
across the PubMed corpus, and exploring description similarities in a database
of clinical trials. Experimental results demonstrate the potential of our
approach for unstructured knowledge discovery in similarity networks.
