On structural information similarity measurements

Abstract

Measuring structural similarities is attracting more and more attention from researchers. In this paper, we define structural information content (SIC) for measuring the structural information of a structure, and introduce topological match degree to measure to what extent a subtree is matched. By recursively computing SICs and thus computing topological match degrees, we evaluate the structural information similarities of data trees to pattern tree. In the paper, we present two algorithms for recursively calculating SICs with computation complexity of O(M), and use examples to instantiate the feasibility of the proposed method.