Abstract
In our earlier attributed generalized tree (AGT) structures, vertex labels (as types) and edge labels (as attributes) embody semantic information, while edge weights express assessments regarding the (percentage-)relative importance of the attributes, a kind of pragmatic information. Our AGT similarity algorithm has been applied to e-Health, e-Business, and insurance underwriting. In this paper, we compare similarity computed by our AGT algorithm with the similarities obtained using: (a) a weighted tree similarity algorithm (WT), (b) graph edit distance (GED) based similarity measure, (c) maximum common subgraph (MCS) algorithm, and (d) a generalized tree similarity algorithm (GT). It is shown that small changes in tree structures may lead to undesirably large similarity changes using WT. Further, GT is found to be not applicable to AGT structures containing semantic as well as pragmatic information. GED and MCS cannot differentiate AGT structures with edges having different directions, lengths, or weights, all taken into account by our AGT algorithm.
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