Hierarchies are a natural way for people to organize information, as
reflected by the common use of ``broader/narrower'' term relation in keyword
thesauri. However, different people and organizations tend to construct
different conceptual hierarchies (e.g., contrast Yahoo! with the UseNet news
hierarchy), and while there are often significant commonalities it is in
general quite difficult to fully reconcile them. We are particularly
interested in the problem of ``docking'' a narrower, more focused and refined
topical hierarchy into a broader one, and describe two algorithms for
accomplishing this task. The first matches hierarchies based on a bipartite
matching algorithm of (textual) features of nodes without consideration of
their hierarchic organization, and the second is based on an attributed tree
matching algorithm which uses both hierarchic structure and node features. We
present experimental results showing the performance of both algorithms on a
set of very different topical hierarchies, all designed to represent the field
of {\tt Computer\_Science}. These show that hierarchic structure does indeed
allow more accurate matches than nodes alone.
Pre-2018 CSE ID: CS2001-0669