Unlike Western M edicine, those in Traditional Chinese M edicine (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.
The difference between Western Medicine (WM) and TCM is that WM focuses on the anatomy of human body, but TCM is based on an entirely diffe rent system of inherent rules. Because of the complicated philosophy, TCM has not had a proper understanding using current computer technologies.
The primary goal of Se mantic Web [
For what we concern, we wish to apply the Semantic Technologies to represent
TCM knowledge, which is ma inly focused on rule reasoning derived fro m TCM
philosophy. For e xa mple , the Five Phase Theory [
A lot of work has also been done to study the causal relationship in TCM . A
stepwise causal adjacent re lationship discovery algorith m [
TCM is rather a static theory model than an ever-evolving statistical one, so an e xpressive causal knowledge model with built-in rules can reveal the nature of TCM better. In this paper, we present an approach to build a TCM knowledge model with the capability of rule reasoning based on property chains using OWL 2. 2
We imple ment the causal TCM knowledge model with five functional layers . Ontology layer gives the basic terminology and assertio ns represented using OWL 2. Association rules are gathered in rules layer. Rule engine layer is recognized as an engine to deal with rules. Method layer do knowledge min ing based on the defined ontology and rules. The utility of causal TCM knowledge model is ought to be the natural representation of TCM knowledge; it is also a reference model for TCM knowledge reasoning and knowledge mining , more importantly.
Our TCM knowledge model is designed based on the basic princip le of the Five Phase Theory and the aim is to enable causal reasoning of TCM. According to the theory, all the things in the universe can be mapped to one of the five e le ments known as Wood, Fire, Earth, Metal, and Water. We define seven top ontology classes and corresponding sub classes to enable later causal reasoning.
In the top class design, “Five Phases” refers to the basic five e le ments in Five Phase Theory. “Environment” defines all kinds of natural e le ments that we use to diagnose diseases. “Body Ele ments” include all the physiological co mponents. “Physiology” describes the related descriptions which describe human body conditions. “Pathology” describes pathologic states of human body. “Treatment” includes Chinese medic ine, rec ipe, rules of treat ment and therapy. “Sy mptoms” contains abnormal human body states.
In OWL 2, applications need to model interactions that are referred as one property “propagating” or being “transitive across” another. For now, we define about 30 object properties, which will help us define the property chains in the reasoning stage. For instance, we have : ObjectPropertyAssertion(:creates :Wood :Fire) to state Wood generates Fire in the theory. These internal properties among them give a basic causal foundation of TCM knowledge model.
As stated above, ontology classes establish a terminology structure for TCM domain knowledge; refined properties connect the terminology nodes tightly to form a knowledge model. 3
The clin ical diagnosis in TCM is ma inly based on the internal ru les. In this paper, we transform the diagnosis process into a layered causal graph.
We define the layered Causal Graph based on the fact that TCM diagnosis ma inly focuses on syndrome. In a causal graph, there are nodes as terminology base, and causal links. Thus, formally a Causal Graph G={V, E}, is defined as: V R, where R={Symptom, Syndrome, Treatment Rule, Therapy, Prescription,
Herbal Medicine}. E U, where U={→,⇢,↝ }.
- X→Y represents that X has a Direct Causation relationship with Y meaning that
X is a direct cause of Y independently. - X⇢Y represents that X has a Logic Causation relationship with Y as co mbined causation with logical operators, which exists from s ymptoms to syndrome. - X↝ Y represents that X has a Weighted Causation relationship with Y that X plays a partial causation in Y, wh ich e xists from previous layers to prescriptions. A Rule Pattern is defined as a direct path fro m X to Z or a logica l path fro m a set of X to Z, which means nodes connected by links can be defined as a rule.
The basic idea o f Fig.1 is that certa in set of sympto ms identifies certa in syndrome or disease (logic causation), and some syndrome or d isease leads to the corresponding rule of treat ment and therapy (direct causation), however the final prescription will be decided fro m the symptoms, the developing syndromes and the therapy together (weighted causation).
Generally, causal reasoning ma kes the TCM diagnosis process into a structural graph, and provides different layers of the graph with suitable algorithms.
In this section we discuss the experimental evaluation of our model. In total, we designed 821 classes, 26 object properties, 134 class assertion axio ms and 78 object property axio ms in our ontology. Since our causal knowledge model is derived fro m TCM diagnostic principles, we evaluated the process using two typical medical use cases in TCM.
Use case 1 (as shown in Fig 2) Input: Angry (single symptom) Output: A causal graph generated by user input as follow.
Description: When our system gets the input, it will search the RDF graph starting with the input node “angry”, that means searching reachable n odes through property chains and pre-defined rules in the generated graph. All the white nodes are the final displayed results. The blue ones are the latent causal links upon each edge, wh ich can be displayed when user clicks the edge.
Use cases 2 (as shown in Fig 3) Input: A set of symptoms Output: One or multiple possible syndromes diagnosed from the input symptoms Description: When user submits a set of symptom, our approach searches possible corresponding syndromes in our knowledge base and output it to the user.
The evaluation results are represented in Chinese in our system, so we depicted them in use cases above using alternative graphs. As we described, use case 1 shows the important princip les in TCM philos ophy, that’s the action cycles between the basic five elements, based on which we conduct knowledge learning and knowledge mining. Our TCM knowledge model gets a satisfying result in TCM knowledge representation, it also has a great potential in knowledge mining. 5
In this paper, we p resent a causal knowledge modeling method that can be applied to TCM diagnosis process. The princip le objective of TCM knowledge modeling is to figure out a formal method to represent Chinese medicine knowledge.
We build a TCM causal knowledge model based on the belief that the underlying causal relations inside TCM ontology can be represented using OWL 2. We defined seven ontology classes and corresponding sub classes based on Five Phase Theory. All the properties are defined according to the key activ ities between key concepts in diagnosis . The relationship between symptoms and diseases or syndromes is the focus in determining the disease. However, the relationship is not pure one-to-one relation, but many to uncertain. As for this reason, we vie wed TCM knowledge as a layered causal graph, part icularly vie wed sets of symptoms as a whole, and our algorithms upon causal graph involves symptom matching and syndrome progress. Our causal knowledge reasoning, which is a method of integrating defined ontology with pre-defined ru les to co mpose a causal graph, can c learly de monstrate the process of TCM diagnosis and shows a potential to do knowledge mining . 6