QRIOM: A QPT-based simulator for composing and reasoning qualitative models for learning organic reactions

Abstract

The work discusses the application of an artificial intelligence technique called qualitative reasoning (QR) and a process-based ontology in constructing qualitative models for organic reaction simulation. We present a framework architecture that uses the QPT ontology as the knowledge representation scheme to model the behaviors of a number of organic reactions. The main focus of this paper placed on the design of two main components (model constructor and reasoning engine) for a tool abbreviated as QRIOM for predicting and explaining organic reactions. The discussion starts by presenting the workflow of the reasoning process and the automated model construction logic. We then move on to demonstrate how the constructed models can be used to reproduce the behavior of organic reactions. Finally, behavioral explanation manifestation is discussed. The simulator is implemented in bi lingual; Prolog is at the backend supplying data and chemical theories while Java handles all front-end GUI and molecular pattern updating.