A spreading activation network model is applied to the problem of reconciling heterogeneous indexing (STI index terms and ApJ subject descriptors) in a database of documents in the field of astronomy. Drawing on evidence from a set of co-indexed documents, a 3-layer feed-forward network is constructed. It includes an input term layer (source vocabulary), document layer, and output term layer (target vocabulary). Results of experiments show that the network can uncover both static, term-to-term relationships, and those that depend on the context of a particular document's indexing. From the static mapping experiment, the asymmetric nature of term mapping is revealed. A visualization tool graphically shows complex term relationships identified by this model. The context-sensitive mapping experiment tests the robustness of the network against the removal of each document node under testing. The performance of the complete network is compared to that of the reduced network. The results imply that mapping is largely dependent on regularities emerging from the entire pattern of connections in the network rather than localist representations. The mapping from specific to general shows better performance than the mapping from general to specific. Several issues related to the model including limitations, application of a learning algorithm, and the generality of the study are discussed.