A Graph Matching Algorithm and Its Application to Conceptual System Translation

Abstract

ABSURDIST II, an extension to ABSURDIST, is an algorithm using attributed graph matching to find translations between conceptual systems. It uses information about the internal structure of systems by itself, or in combination with external information about concept similarities across systems. It supports systems with multiple types of weighted or unweighted, directed or undirected relations between concepts. The algorithm exploits graph sparsity to improve computational efficiency. We present the results of experiments with a number of conceptual systems, including artificially constructed random graphs with introduced distortions.

Connecting Concepts to Each Other and the World

“…Initial results with the graph-based version of ABSURDIST suggest a number of interesting trends (Feng, Goldstone, & Menkov, 2004). First, as expected, adding multiple types of relations such as is-a and has-a (see Figure 12.7) allows ABSURDIST to more accurately construct translations than it did with the single, generic similarity relation.…”

CyberMaster: An Expert System to Guide the Development of Cybersecurity Curricula

“…These factors make it difficult to match elements between maps in comparison, retrieval, and merging processes [30]. Matching algorithms for the knowledge elements in educational concept maps were introduced in [30][31][32]. Concept maps and expert systems are soft tools used for knowledge modelling [33] where establishing a concept map was considered as the first step in curriculum development [34].…”

Towards a General Framework for Effective Solutions to the Data Mapping Problem

“…In many ways, ongoing research efforts towards building universal knowledge bases are a continuation of this long-standing effort towards resolving, once-and-for-all, syntactic and semantic data heterogeneity [55]. Of course, outside of information systems research, investigators in linguistics and cognitive science have also focused intense sustained effort on resolving the inherent problems of mapping between heterogeneous conceptual models in biological and artificial communicative systems, e.g., [17].…”