=Paper=
{{Paper
|id=None
|storemode=property
|title=Causal Knowledge Modeling for Traditional Chinese Medicine using OWL 2
|pdfUrl=https://ceur-ws.org/Vol-660/paper4.pdf
|volume=Vol-660
}}
==Causal Knowledge Modeling for Traditional Chinese Medicine using OWL 2==
https://ceur-ws.org/Vol-660/paper4.pdf
Causal Knowledge Modeling for Traditional Chinese
Medicine using OWL 2
Peiqin Gu
College of Computer Science, Zhejiang University , P.R.China
gupeiqin@zju.edu.cn
Abstract. 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.
Keywords: Causal knowledge modeling, TCM , Rule reasoning, OWL 2
1 Introduction
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 different system of
inherent rules. Because of the complicated philosophy, TCM has not had a proper
understanding using current computer technologies.
The primary goal of Semantic Web [1] is to use URIs as a universal space to
name anything, expanding fro m using URIs for web pages to URIs for “real objects
and imag inary concepts ”, as phrased by Berners-Lee. A mong those efforts done by
W3C group, Resource Description Framework (RDF) [2] provides data model
specifications and XML-based serialization syntax, Web Ontology Language (OW L)
[3] enables the defin ition of do main ontologies and sharing of do main vocabularies.
The OWL 2 Web Ontology Language, informally OW L 2, provides a possible
solution for rule reasoning using property chains.
For what we concern, we wish to apply the Semantic Technologies to represent
TCM knowledge, which is mainly focused on rule reasoning derived fro m TCM
philosophy. For example, the Five Phase Theory [4] of TCM divides all the things in
the whole world to five types, which are Wood, Fire, Earth, Metal, and Water. The
system of five phases was used for describing interactions and relationships between
phenomena.
A lot of work has also been done to study the causal relationship in TCM . A
stepwise causal adjacent relationship discovery algorith m [ 5] has been developed to
study correlation between composition and bioactivity of herbal med icine and identify
active co mponents fro m the co mplex mixture of TCM . Ch inese Medical Diagnostic
System (CM DS) [6] contains an integrated medical ontology and the prototype of it
can diagnose about 50 types of diseases by using over 500 rules and 600 images for
various diseases. Wang et al. [7] developed a self-learning expert system fo r diagnosis
in TCM using a hybrid Bayesian network learning algorith m, Naï ve-Bayes classifiers
with a novel score-based strategy for feature selection and a method for mining
constrained association rules. However, these researches focused on applying
mathematical methods on TCM knowledge mining and learning, so domain ontology
only acts as knowledge base without self-learning capability, although building
inherent knowledge links can be applicable using current ontology language.
TCM is rather a static theory model than an ever-evolving statistical one, so an
expressive 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 Causal TCM Knowledge Modeling
We implement 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 elements 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 elements in Five
Phase Theory. “Environment” defines all kinds of natural elements that we use to
diagnose diseases. “Body Elements” 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 medicine, recipe, 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 Causal Reasoning
The clin ical diagnosis in TCM is mainly 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
mainly 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 exists from previous layers to prescriptions.
A Rule Pattern is defined as a direct path fro m X to Z or a logical path fro m a set
of X to Z, which means nodes connected by links can be defined as a rule.
Fig. 1.The causal reasoning graph in TCM knowledge model.
The basic idea o f Fig.1 is that certain set of sympto ms identifies certain
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 makes the TCM diagnosis process into a structural
graph, and provides different layers of the graph with suitable algorithms.
4 Evaluation
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.
Fig. 2.Use case 1
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.
Fig. 3.Use case 2
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 Conclusion
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 viewed TCM
knowledge as a layered causal graph, part icularly viewed 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 clearly demonstrate
the process of TCM diagnosis and shows a potential to do knowledge mining.
6 Acknowledgments
Thanks to the Grid Co mputation Group in CCNT Lab of Zhejiang Un iversity.
This paper is supported by NSFC61070156, China 863 program with
No.2009AA011903.
References
1. T.Berners-Lee, J.Hendler, and O.Lassila, J: The Semantic Web. Scientific American. (2001).
2. Frank M anola, Eric M iller, S: RDF Primer, http://www.w3.org/TR/rdf-primer/, (2004)
3. F.van Harmelen, S.Bechhofer, J.Hendler,et.al. S: OWL Web Ontology Language Reference.
http://www.w3.org/TR/owl-ref/, (2002).
4. EB/OL: Wikipedia-Wu Xing. http://en.wikipedia.org/wiki/Wu_Xing
5. Cheng Y, Wang Y, Wang X. J: A Causal Relationship Discovery -based Approach to
Identifying Active Components of Herbal M edicine. Comput.Biol.Chem, Vol.30, pp.148--
154(2006).
6. M . Huang, M . Chen. J: Integrated Design of the Intelligent Web-based Chinese M edical
Diagnostic system (CMDS)-Systematic Development for Digestive Health. Expert Syst.
Appl. Vol.32, pp.658--673(2007).
7. X. Wang, H. Qu, P. Liu, et al. J: A Self-learning Expert System for Diagnosis in Traditional
Chinese M edicine. Expert Syst. Appl, Vol.26. pp.557--566(2006).