Use semantic decision tables to improve meaning evolution support systems

Abstract

Meaning evolution support systems (MESSs) have been recently introduced as a real time, scalable, community-based cooperative systems to support the ontology evolution. In this paper, we intend to address the problems of accuracy and effectiveness by using Semantic Decision Tables (SDTs). A SDT separates general decision rules from the processes, bootstraps policies and template dependencies in the whole system. Recently, DOGMA-MESS ('developing ontology-grounded methodology and applications' framework based 'MESS') are developed at VUB STARLab as a collection of MESS. We embed SDTs in DOGMA-MESS to illustrate our approach. SDTs play the roles in both top-down and bottom-up processes of the meaning evolution cycle. The decision rules that consist of templates dependency rules are mainly responsible for the top-down process execution. The bottom-up process execution relies on the ones that contain the concept lifting algorithms.