  Transitive text mining - also named Swanson Linking (SL) after its primary
and principal researcher - tries to establish meaningful links between
literature sets which are virtually disjoint in the sense that each does not
mention the main concept of the other. If successful, SL may give rise to the
development of new hypotheses. In this communication we describe our approach
to transitive text mining which employs co-occurrence analysis of the medical
subject headings (MeSH), the descriptors assigned to papers indexed in PubMed.
In addition, we will outline the current state of our web-based information
system which will enable our users to perform literature-driven hypothesis
building on their own.
