Docking topical hierarchies: A comparison of two algorithms for reconciling keyword structures

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: CS2001-066