Abstract :
Hypothesis generation, a crucial initial step for making
scientific discoveries, relies on prior knowledge, experience,
and intuition. Chance connections made between
seemingly distinct subareas sometimes turn out to be
fruitful. The goal in text mining is to assist in this process
by automatically discovering a small set of interesting
hypotheses from a suitable text collection. In this report,
we present open and closed text mining algorithms that
are built within the discovery framework established by
Swanson and Smalheiser. Our algorithms represent topics
using metadata profiles. When applied to MEDLINE,
these are MeSH based profiles. We present experiments
that demonstrate the effectiveness of our algorithms.
Specifically, our algorithms successfully generate
ranked term lists where the key terms representing
novel relationships between topics are ranked high
