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Journal of Chinese Integrative Medicine ›› 2013, Vol. 11 ›› Issue (5): 352-365.doi: 10.3736/jintegrmed2013051

• Methodology • Previous Articles     Next Articles

An improved association-mining research for exploring Chinese herbal property theory: Based on data of the Shennong’s Classic of Materia Medica

Rui Jina,b, Zhi-jian Linb, Chun-miao Xueb, Bing Zhangb()   

  1. a. Department of Pharmacy, Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China
    b. School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100029, China
  • Received:2012-12-28 Accepted:2013-04-15 Online:2013-09-10 Published:2013-09-15

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Knowledge Discovery in Databases is gaining attention and raising new hopes for traditional Chinese medicine (TCM) researchers. It is a useful tool in understanding and deciphering TCM theories. Aiming for a better understanding of Chinese herbal property theory (CHPT), this paper performed an improved association rule learning to analyze semistructured text in the book entitled Shennong's Classic of Materia Medica. The text was firstly annotated and transformed to well-structured multidimensional data. Subsequently, an Apriori algorithm was employed for producing association rules after the sensitivity analysis of parameters. From the confirmed 120 resulting rules that described the intrinsic relationships between herbal property (qi, flavor and their combinations) and herbal efficacy, two novel fundamental principles underlying CHPT were acquired and further elucidated: (1) the many-to-one mapping of herbal efficacy to herbal property; (2) the nonrandom overlap between the related efficacy of qi and flavor. This work provided an innovative knowledge about CHPT, which would be helpful for its modern research.

Key words: Traditional Chinese medicine, Chinese herbal property theory, Association rule learning, Knowledge discovery, Data mining

Figure 1

The semistructured text of ginseng in the book entitled Shennong’s Classic of Materia MedicaThis semistructured record was divided by the underlined semantic elements into six parts: ① herbal name, ② herbal flavor, ③ herbal qi, ④ herbal efficacy, ⑤ alias and ⑥ place of origin."

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Figure 2

The sensitivity of parameters (a) presented the number of itemsets along with the increasing support threshold and (b) presented the confidence distribution of frequent patterns with the minisup of 3."

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Figure 3

The proportion of association rules of specified formsThe proportion of rules with efficacy in the antecedent was 85.83% while that of efficacy in the consequent was 14.17%."

Figure 4

The many-to-one mapping of herbal efficacy to cold qiThe cold qi of different herbs may be inferred from different kinds of efficacy. The presented medicinal herbs are Dahuang (Rhei radix et rhizoma), Zhizi (Gardeniae fructus), Cheqianzi (Plantaginis semen), Huangbai (Phellodendri chinensis cortex) and Zhimu (Anemarrhenae rhizoma)."

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Figure 5

The example of “isotropic” herbs, “anisotropic” herbs and their relationshipsThe strongly related efficacy attributes (in rectangles) are marked next to the corresponding herbal property (in ovals). The herbs (in circles) are connected with its efficacy by straight lines. The “isotropic” herbs are represented by the red, green and blue circles. They are pungent-hot herbs Wuzhuyu (WZY, Euodiae fructus), Fuzi (FZ, Aconiti lateralis radix praeparata) and Xixin (XX, Asari radix et rhizoma); sweet-neutral herbs Puhuang (PH, Typhae pollen) and E-jiao (EJ, Asini corii colla); and bitter-cold herbs Dahuang (DaH, Rhei radix et rhizoma) and Zhizi (ZZ, Gardeniae fructus). The “anisotropic” herbs are represented by the purple circles. They are sweet-hot herb Shizhongru (SZR, Stalactitum), pungent-cold herb Ningshuishi (NSS, Calcitum seu Gypsum rubrum), bitter-hot herbs Mahuang (MH, Ephedrae herba) and Baizhu (BZ, Atractylodis macrocephalae rhizoma), sweet-cold herbs Maogen (MG, Imperatae rhizoma) and Dihuang (DiH, Rehmanniae radix) and bitter-neutral herb Chaihu (CH, Bupleuri radix)."

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