Causal Knowledge Modeling for Traditional Chinese Medicine using OWL 2
Unlike Western Medicine, those in Traditional Chinese Medicine (TCM) are based on inherent rules or patterns, which can be considered as causal links. Existing approaches tend to apply computational methods on semantic ontology to do knowledge mining, but it cannot perfectly make use of internal principles in TCM. When it comes to knowledge representation, we can transform this inherent knowledge into causal graphs. In this paper, we present an approach to build a TCM knowledge model with the capability of rule reasoning using OWL 2. In particular, we focused on the causal relations among syndrome and symptoms, changes between syndromes. We evaluated our approach by giving two typical use cases and implemented them using Jena, a Java framework supporting RDF, OWL, and including a rule-based inference engine. The evaluation results suggested that our approach clearly displayed the causal relations in TCM and shows a great potential in TCM knowledge mining.