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Similarity of Attributed Generalized Tree Structures: A Comparative Study

  • Mahsa KianiEmail author
  • Virendrakumar C. Bhavsar
  • Harold Boley
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9371)

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.

Keywords

Tree similarity Attributed generalized tree Generalized tree similarity Weighted tree similarity 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mahsa Kiani
    • 1
    Email author
  • Virendrakumar C. Bhavsar
    • 1
  • Harold Boley
    • 1
  1. 1.Faculty of Computer ScienceUniversity of New BrunswickFrederictonCanada

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