Self-organized formation of topographic maps for abstract data, such as words, is demonstrated in this work. The semantic relationships in the data are reflected by their relative distances in the map. Two different simulations, both based on a neural network model that implements the algorithm of the selforganizing feature maps, are given. For both, an essential, new ingredient is the inclusion of the contexts, in which each symbol appears, into the input data. This enables the network to detect the "logical similarity" between words from the statistics of their contexts. In the first demonstration, the context simply consists of a set of attribute values that occur in conjunction with the words. In the second demonstration, the context is defined by the sequences in which the words occur, without consideration of any associated attributes. Simple verbal statements consisting of nouns, verbs, and adverbs have been analyzed in this way. Such phrases or clauses involve some of the abstractions that appear in thinking, namely, the most common categories, into which the words are then automatically grouped in both of our simulations. We also argue that a similar process may be at work in the brain.