Towards freely and correctly adjusted Dijkstra's algorithm with semantic decision tables for ontology based data matching

Researches on ontology based data matching have been recently explored. In this paper, we will focus on how to adapt Dijkstra's algorithm for matching two data sets and how to freely adjust the parameters of Dijkstra's algorithm by the end users. A novel approach of using semantic decision tables for the adjustment will be presented and discussed. This particular approach provides a generic, user friendly, simple yet powerful solution to control an algorithm in an elegant way. In order to present the matching result, we propose a formula called GRASIM, which uses the travel cost of the shortest path, to calculate the similarities between two data sets.