Construction and Application of the Multidimensional Table for Knowledge Discovery in Ancient Chinese Books on Materia Medica

doi:10.4236/eng.2013.510B001

Paper Menu >>

Journal Menu >>

Engineering, 2013, 5, 1-6

http://dx.doi.org/10.4236/eng.2013.510B001 Published Online October 2013 (http://www.scirp.org/journal/eng)

Construction and Application of the Multidimensional

Table for Knowledge Discovery in Ancient Chinese Books

on Materi a Medica

Rui Jin1,2, Qian Lin3*, Jun Zhou1, Boyu Sun1, Bing Zhang1*

1School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China

2Department of Pharmacy, Beijing Shijitan Hospital, Beijing, China

3School of Mathematical Science, Peking University, Beijing, China

Email: *[email protected], *[email protected]

Received August 2012

ABSTRACT

Knowledge discovery, as an increasingly adopted information technology in biomedical science, has shown great

promise in the field of Traditional Chinese Medicine (TCM). In this paper, we prov ide d a kind of multidimensional

table which was well suited for organizing and analyzing the data in ancient Chinese books on Materia Medica. More-

over, we demonstrated its capability of facilitating further mining works in TCM through two illustrative studies of

discovering meaningful patterns in the three-dimensional table of Shennong’s Classic of Materia Medica. This work

might provide an appropriate data model for the development of knowledge discovery in TCM.

Keywords: Multidimensional Table; TCM; Herbal Medicine; Data Mining; Knowledge Di s c overy

1. Introduction

Data mining and knowledge discovery, as incremental

adopted information technologies in biomedical science,

have shown great promise in the field of Traditional

Chinese Medicine (TCM) for years. Based on a different

view toward human life and disease, TCM has developed

a distinct medical system for diagnosis and treatment

during thous a nds of years, whic h has accumulated a large

number of medical and pharmaceutical data [1]. In the

past years, it has been increasingly adopted as an impor-

tant complementary healing therapy around the world [2]

and has attracted researchers among different areas to

mine the “knowledge gold” buried in TCM data mo u n -

tains [3-5]. Thus, data mining techniques are believed to

be able to bridge the gap between the availability of large

amounts of data and the difficulty of obtaining novel

knowledge about TCM, especially the medical theory

such as yin-yang and five elements.

Learning rich dialectical thoughts from the ancient

Chinese philosophies, TCM views the world and human

body as a whole and analyzes their relationship with yin-

yang and five elements theory. These theories build a

universal foundation for the specific theories related to

the diagnosis and treatment, such as syndrome differen-

tiation theory, Zang Fu theory, and Chin ese herbal medi-

cine theory [1]. Among them, the Chinese herbal medi-

cine theory (herbal property, compatibility, the multiple

effectiveness of herbal medicine, etc.) is believed to be a

breakthrough in TCM modernization, which is worthy of

further investigation. Thus , Chinese Herbal Medicine

Informatics (CHMI) has arisen gradually [6-8] and an-

cient Chinese books on Materia Medica, the conventional

media storing the information of medicinal herbs, are

always the preferred materials for study.

Shennong’s Classic of Materia Medica (SCMM ), also

known as Shennong Bencao Jing, is among the great

classics of herbal pharmacology and the earliest extant

one. The book collects 365 kinds of Chine se medicines

and involves many aspects of medicines such as alias, qi

and flavor, efficacy and their origins. More than 170

kinds of diseases are discussed, including diseases of

internal medicine, surgery, gynecology, pediatrics, etc.

[9]. Since many of the recorded herbs are still used in

TCM therapies currently, SCM M has received sufficient

attention in modern research. However, due to the an-

cient Chinese vocabularies, expert data cleansing and

integration are needed for accessibility to modern re-

searchers.

Moreover, to be more effective and valuable, the cre-

dibility of the data source and the contribution to new

knowledge acquisition are required in the process of data

mining. For knowledge discovery in TCM, three aspects

of data quality should be highlighted to improve data

*Corresponding a uthor.

R. JIN ET AL.

credibility including representation granularity, repre-

sentation consistency and completeness [10]. Another

key issue is the transformation from data mining results

generated by the computer into novel TCM knowledge.

As a solution, the two-cycle model was provided by

Wang in 2008 [11] who has attached importance to the

collaboration of medical researchers and data mining

researchers.

In this paper, w e intended to establish a kind of multi-

dimensional table to manage herbal information con-

tained in Materia Medica books, as well as to permit data

to be easily accessed and analyzed. Taking SCMM for

example, we constructed the three-dimensional table that

presented the major aspects of herbs including herbal qi,

herbal flavor and herbal efficacy. Furthermore, we ap-

plied the three-dimensional table of SCMM to mining

novel knowledge related to Chinese herbal theory. This

framework might provide as a helpful tool for informa-

tion management and unde r standi ng i n T C M .

The rest of the paper is organized as follows. Section 2

described the process of constructing the multidimen-

sional table for appropriate organization of information

contained in SCMM. Section 3 presented two application

examples involving association rules mining and cluster-

ing analysis. Finally we pro vided the conclusions in Sec-

tion 4.

2. The Construction of Multidimensional

Table

Ancient Chinese materia medica books are among the

most important resources of TCM for data mining, which

constitute the foundations of CHMI. As a practical ma -

nual of TCM drug therapy, the information about herbal

name and botanical origins recorded in the book guaran-

tees the fit medicinal herbs, while the information about

herbal property and efficacy reflects the direct expe-

riences of TCM practitioners on clinical drug use [12,13].

Actually, in the view of data management, the text in

these books shares common features of semi-structured

data, which contains tags or other markers to separate

semantic elements and enforce hierarchies of records and

fields within the data [14]. For example, SC MM is com-

posed of 365 medicine records prepared by classical

Chinese words. Each re cord is written in accordan ce with

the record format which can be divided into six parts

including herbal name, herbal qi, herbal flavor, herbal

efficacy, alias and source land (Figure 1). The first four

parts which formed the main body of Chinese herbal

theory were selected in this work to construct the multi-

dimensional table.

2.1. Table Structure

A multidimensional table is a multidimensional array

consisting of records (rows) and fields (columns), which

is suited for organizing and analyzing the data in ancient

Chinese books on Materia Medica. In the data table of

SCMM, each row represented a single herbal medicine.

Herbal qi, herbal flavor and herbal efficacy, which are

among the most significant parameters to define the clin-

ical performances of medicinal herbs, were employed as

fields. In addition, each of the first two fields could be

split into five categories due to its structur ed data model.

However, the field of efficacy presented as semi-struc-

tured text, would be split into a determinate number of

categories after appropriate data integration. Therefore,

the resulting table would have three dimensions, since

each categorized variable represented one dimension.

The ultimate data model was shown in Table 1, which

also contained a unique identifier (Herb ID) and herbal

name. The concrete information of each dimension is as

follows:

1) Herbal qi dimension: It is th e structured data which

has five attributes (equiva lent to categories in the field in

this paper) including cold, cool, neutral, warm and hot.

Only one attribute can serve as the marker for each herb

in this dimension.

Figure 1. Herbal medicine recor ds in Shennong’s classic of materia medica.

Table 1. Data model of SCMM.

Column 1 Column 2 Column 3 Column 4 Column 5

Herb ID Herb name Herb nature Herb flavor Efficacy

R. JIN ET AL.

2) Herbal flavor dimension: It is the structured data

which has five attributes including pungent, sweet, sour,

bitter and s alty. Only one attribute can serve as the marker

for each herb in this dimension.

3) Herbal efficacy dimension: It is the semi-structured

data which can be divided into a finite number of

attributes after data integration. Several attributes can

serve as the markers for each herb in this dimension.

2.2. Data Preprocess

Since most of the ancient Chinese Materia Medica b ooks

are prepared by classical Chinese and provided with dif-

ferent versions, data preprocess (e.g. data cleaning, data

integration and annotation) is indispensable for ensuring

data quality. In this wor k, regarding to synonyms of effi-

cacy terms in Classical Chinese, some ancient and con-

temporary references including Zhu Bing Yuan Hou Lun

[15], Internal Medicine of TCM [16], Surgery of TCM

[17], Obstetrics and Gynecology of TCM [18] and two

proofreading and annotation books for SCMM [19,20]

were employed to achieve representation consistency.

Finally, 196 items were acquired for attributes in efficacy

dimension. Thus, semi-structured data records presented

in Figure 1 can be converted into a data table shown in

Table 2.

After the selection of defined attributes in three di-

mensions separately, a kind of three-dimensional table

was constructed in an Excel file format. The row of the

table represented the information of a single herbal med-

icine. The medicine was located in the table using Boo-

lean values whose expression was evaluated to 0 if the

medicine did not have the corresponding attribute, 1 if it

have (Table 3). Taking ginseng for example, the value of

the cell identified by the row of ginseng and the column

(attribute) of cool was 1 while other values in this di-

mension were 0 because the herbal qi of ginseng was

cool.

3. The Application of Multidimensional

Table

Above all, the digitization of information in ancien t Chi-

nese materia medica books was achieved appropriately

Table 2. Data table of herbal me dicine records in SCMM.

Herb ID Herb name Herbal qi Herbal flavor Herbal efficacy

1 Radix Ginseng Cool Sweet Tonifying the middle qi, Nourishing essence-spirit, Settling soul and

spirit, Tranquilizing, Removing pathogenic qi, Improving vision,

Enhancing the wisdow, Promoting longevity

2 Radix Glycyrrhizae Neutral Sweet

Removing pathogenic qi in Zang and Fu, Strengthening muscles and

bones, Tonifying qi , Curing war wounds, Removing toxicity, Promoting

longevity

3 Radix Scutellaria Neutral Bitter Clearing heat, Treating jaundice, Curing diarrea, Removing water

retention, Curing amenorr hea, Treating sore and ulcer, Treating unhealed

sore

4 Fructus Gardenia Cold Bitter

Removing pathogenic qi in Zang, Clearing heat, Treating sore and ulcer,

Curing leprosy, Relieving reddened complexion, Treating acne

erythematosa

5 Radix Aconiti Carmichaeli Hot Pungent

Warming the middle qi, Removing pathogenic qi, Relieving cough with

dyspnea, Curing aggregation-accumulation, Curing impediment disease

and wilting disease, Curing war wounds

6 Fructus Evodiae Hot Pungent Warming the middle qi, Relieving cough with dyspnea, Curing cold and

heat, Treating fixed impediment and blood impedime nt, dispersing wind

pathogen, Relieving pain, Releasing the exterior

Table 3. An example of the three-dimensional table.

Herb

ID Herb

na me

Herbal qi Herbal flavor Herbal efficacya

Cold Cool Neutral Warm Hot Sour Bitter Sweet Pungent Salty Promoting

longevit y

Removing

pathogenic

Warming

the

middle qi

Clearing

heat

Curing

war

wounds

Radix

Ginseng

0 1 0 0 0 0 0 1 0 0 1 1 0 0 0

Radix

Glycyrrhi zae 0 0 1 0 0 0 0 1 0 0 1 1 0 0 1

Radix

Scutellaria 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0

Fructus

Gardenia 1 0 0 0 0 0 1 0 0 0 0 1 0 1 0

Radix Aconiti

Carmichaeli

0 0 0 0 1 0 0 0 1 0 0 1 1 0 1

Fructus

Evodiae 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0

aFive attributes in herbal efficacy dimension were chosen for display.

R. JIN ET AL.

by the multidimensional table, which could facilitate

further data mining works. The complete three-dime n-

sional table of SCMM consisted of 365 herb records, in-

cluding 5 attributes in herbal qi dimension , 5 attributes in

herbal flavor dimension and 196 attributes in herbal effi-

cacy dimension. Then, two data mining researches, an

association rules mining [21] and a cluster analysis [22],

were implemented to search for correlations between

attributes and between records respectively (Figure 2).

They would contribute to the acquisition of novel know-

ledge about Chines e herbal theory.

3.1. Association Rules Mining

In this section, frequent patterns and valued association

rules between attributes in the dimension of herbal

qi/flavor and herbal efficacy were mined. These kinds of

association rules demonstrated the strong relations be-

tween herbal property and herbal efficacy, promoting the

understanding of Chinese herbal theory. Setting the

proper parameters, we acquired 115 strong association

rules by the Apriori algorithm (Table 4), which pre-

sented the evidence to discriminate the qi/flavor of the

medicinal herb with specific efficacy. As we can see,

some efficacy attributes in Table 2 were among them

such as promoting longevity, clearing heat, warming the

middle qi, etc.

3.2. Cluster Analysis

In this section, a classification study was implemented by

using se mi -supervised incremental clustering algorithm.

Calculating the jaccard’s index of similarity between

every two herb records, we first selected the micro-clus-

ters whose members had exceptionally close correlations.

Then a k-nearest neighbor algorithm (k = 3) was used to

Figure 2. Two data mining studies based on the t hree-dimensional table of SCMM.

Table 4. Strong association rules.

Form Strong association rules

Total number Examples

⇒

Efficacy 1 Neutral

⇒

Promoting longevity

Flavor

⇒

Efficacy 3 Sweet

⇒

Tonifying qi

Qi∧Flavor

⇒

Efficacy 18 Sweet∧Neutral

⇒

Tonifying qi

Efficacy

⇒

Qi 38 Warming the middle qi

⇒

Hot; Promoting longevity

⇒

Neutral;

Removing toxicity

⇒

Neutral; Clearing heat

⇒

Cold

Efficacy

⇒

Flavor 46

Warming the middle qi

⇒

Pungent; Relieving cough with dyspnea

⇒

Pungent; Nourishing essence-spirit

⇒

Sweet; Removing water

retention

⇒

Bitter

Efficacy

⇒

Qi∧Flavor 9 Warming the middle qi

⇒

Pungent∧Hot; Resolving hard mass in

stomach and intestine

⇒

Bitter∧Cold

R. JIN ET AL.

classify the rest of the herbs. The results showed that 2 53

herbal medicines were reasonably classified as 14 types

such as sort of invigoration, clearing heat, diuresis, treat-

ing impediment disease and treating gynecological dis-

ease, while the other 112 medicines were classified into

112 individual types. The same high similarity to differ-

ent known types might be the main reason for those ind i-

vidual herbs. Table 5 showed the major clusters involv-

ing more than 10 herbs.

4. Conclusion

Data mining is a promising technology which can be

applied in analyzing vast amounts of TCM data for in-

vestigating novel knowledge. In this paper, we provided

a kind of multidimensional table that was suited for the

data in ancient Chinese materia medica books, in order to

assist resear chers to manage the data in an efficient way.

Moreover, we also introduced two illustrative studies of

mining meaningful patterns in the three-dimensional ta-

ble of SCMM. The results provided evidence that the

multidimensional table could facilitate data mining

works in TCM.

Table 5. Representative clusters.

Type

Clusters

Total

number Examples Efficacy

1 105 Radi x ginseng

Radix Glycyrrhizae Invigoration

2 50 Radix Scutellaria

Fructus Gardenia Clearing heat

3 30 Rhizoma Ligustici Wallichi

Radix Angelicae Sinensis T

reating gynecological

disease

4 15 Rhizoma Podophyllum

Scolopendra Subspinipes T

reating strange diease

caused by ghost

5 12 Nidus Vespae

Calculus Bovis Treating fright

palpitation

6 11 Folium Pyrrosiae

Semen Plantaginis Diuresis

7 10 Radix Aconiti Carmichaeli

Fructus Evodiae Treating impediment

disease

5. Acknowledgements

This work is supported by China 973 project (2007-

CB512605), the Scientific Research Innovation Team of

Beijing University of Chinese Medicine (2011-CXTD-14)

and Hui-Chun Chin and Tsung-Dao Lee Chinese Under-

graduate Research Endowment (second author).

REFERENCES

[1] X. Z. Zhou, Y. H. Peng and B. Y. Liu, “Text Mining for

Traditional Chinese Medical Knowledge Discovery: A

Survey,” Journal of Biomedical Informatics, Vol. 43,

2010, pp. 650-660.

http://dx.doi.org/10.1016/j.jbi.2010.01.002

[2] National Center for Complementary and Alternative Me-

dicine, “The Use of Complementary and Alternative Me-

dicine in the United States,” 2008.

[3] Y. Feng, Z. H. Wu, X. Z. Zhou, Z. M. Zhou and W. Y.

Fan, “Knowledge Discovery in Traditional Chinese Med-

icine: State of the Art and Perspectives,” Artificial Intel-

ligence in Medicine, Vol. 38, 2006, pp. 219-236.

http://dx.doi.org/10.1016/j.artmed.2006.07.005

[4] S. Lukman, Y. L. He and S. C. Hui , “Computational Me-

thods for Traditional Chinese Medicne: A Survey,” Com-

puter Methods and Programs in Biomedicine, Vol. 88,

2007, pp. 283-294.

[5] S. Li, B. Zhang, D. Jiang, Y. Y. Wei and N. B. Zhang,

“Herb Network Construction and Co-Module Analysis

for Uncovering the Combination Rule of Traditional

Chinese Herbal Formulae,” BMC Bioinformatics, Vol. 11,

Suppl 11, No. S6 , 2010, pp. 1-12,.

http://dx.doi.org/10.1016/j.cmpb.2007.09.008

[6] Y. Y. Cheng, X. H. Fan a nd H. B. Qu, “Discussion on the

Establishment and Development of Chinese Herbal Med-

icine Informatics,” Chinese Journal of Information on

TCM, Vol. 10, No. 2, 2003, pp. 84-92.

http://dx.doi.org/10.1186/1471-2105-11-S11-S6

[7] Z. P. Ding, J. H. Wang and Y. J. Qiao, “Explanation of

Chinese Herbal Medicine Informatics,” Chinese Journal

of Information on TCM, Vol. 10, No. 4, 2003, pp. 92-94.

[8] R. Fang, “Progress in TCM Informatics,” Chinese Jour-

nal of Information on TCM, Vol. 16, No. 1, 2009, pp. 2-7.

[9] ChineseCultureOnline.org, “Chinese Medicine Book:

Shennong Emperor’s Classic of Materia Medica,” 2013.

http://www1.chinaculture.org/library/2008-01/31/content

_26874.htm

[10] Y. Feng, Z. H. Wu, H. J. Chen, T. Yu, Y. X. Mao and X.

H. Jiang, “Data Quality in Traditional Chinese Medicine,”

Proceedings of IEEE Symposium on BioMedical Engi-

neering and Informatics (BMEI 2008), IEE E Press, 2008,

pp. 255-259.

http://dx.doi.org/10.1109/BMEI.2008.268

[11] H. Wang and S. H. Wang, “Medical Knowledge Acquisi-

tion through Data Mining,” Proceedings of IEEE Sympo-

sium on IT in Medicine and Education (ITME 2008),

IEEE Press, 2008, pp. 777-780.

[12] B. Zhang, Z. J. Lin, H. Q. Zhai and J. M. Huang, “Re-

search of Chinese Medicine Property Theory Based on

the ‘Three-Element’ Hypothesis,” China Journal of Chi-

nese Materia Medica, Vol. 33, No. 2, 2008, pp. 221-223.

[13] R. Jin, B. Zhang, X. Q. Liu, S. M. Liu, X. Liu, L. Z. Li, Q.

Zhang and C. M. Xue, “Study of Biological Performance

of Chinese Materia Medica with Either a Cold or Hot

Property Based on the Three-Element Mathematical Ana-

lysis Model,” Chinese Journal of Integrative Medicine,

Vol. 9, No. 7, 2011, pp. 715-724.

http://dx.doi.org/10.3736/jcim20110704

[14] Wikipedia (Semi-Structured Data), 2013.

http://en.wikipedia.org/wiki/Semi-structured_data

[15] Y. F. Chao, “Treatise on the Pathogenesis and Manifesta-

R. JIN ET AL.

tions of Diseases. One Hundred Classics of Traditional

Chinese M e di c ine. Edited by China Association of Chi-

nese Medicine,” Huaxia Publishing House, Beijing, 2008.

[16] Y. Y. Wang and Z. L. Lu, “Internal Medicine of Tradi-

tional Chinese Medicine,” People’s Medical Publishing

House, Beijing, 1999.

[17] X. H. Tan and D. M. Lu, “Surgery of Traditional Chinese

Medicine,” People ’s Medical Publishing House, Beijing,

1999.

[18] M. R. Liu and W. X. Tan, “Obstetrics and Gynecology of

Traditional Chinese Medicin,” People’s Medical Publish-

ing House, Beijing, 2001.

[19] G. G. Gu, “Shennong’s Classic of Materia Me dica, Anno-

tated by P. J. Yang,” Academy Press, Beijing, 2007.

[20] Z. J. Shang, “Annotations of Shennong’s Classic of Ma-

teria Medica,” Academy Press, Beijing, 2008.

[21] R. Jin, Q. Lin, B. Zhang, X. Lin, S. M. Liu, Q. Zha o and

X. L. Liu, “A Study of Association Rules in Three-Di-

mensional Property-Taste-Effect Data of Chinese Herbal

Medicines Based on Apriori Algorithm,” Chinese Journal

of Integrative Medicine, Vol. 9, No. 7, 2011, pp.794-803.

http://dx.doi.org/10.3736/jcim20110715

[22] R. Jin, B. Zhang, C. M. Xue, S. M. Liu, Q. Zhao and K.

Li, “Classification of 365 Chinese Medicines in Shen-

nong’s Materia Medica Classic Based on a Se mi -Super-

vised Incremental Clustering Method,” Chinese Journal

of Integrative Medicine, Vol. 9, No. 6, 2011, pp. 665-674.

http://dx.doi.org/10.3736/jcim20110614