The Development of Semantic Thai Herb Knowledge Mining for Treatment
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Abstract
Using Thai herbs for treating is an alternative medicine that helps take care of people’s preliminary health and relieves them from diseases and symptoms. This is an important topic that leads to operating this research aimed at: 1) creating the ontology and the rules for searching the semantic Thai herb knowledge, 2) developing semantic Thai herb knowledge mining, and 3) assessing the efficiency of extracting Thai herb semantic knowledge and classifying the Thai herbs’ class. The Thai herb knowledge was collected from 100 websites to build an ontology and extract the semantic knowledge using SWRL with natural language processing. The performance is measured in extracting the semantic knowledge and predicting the classification of herb classes using the neural network (NN), the support vector machine (SVM), the K-nearest neighbor (KNN), and the decision tree (DT). The results show that Thai herb ontology was divided into 2 knowledge classes and comprised eight knowledge nodes. SWRL rules were created to extract the knowledge structures identifying the herb data pattern in 3 types: Zingiber montanum, Tiliacora triandra, and Andrographis paniculate. The ML processing on semantic Thai herb knowledge mining consisted of 4 parts: 1) semantic processing on ontology, 2) extracting the knowledge mining, 3) creating the Thai herb dataset, and 4) predicting the results using ML. The results of assessing the semantic Thai herb knowledge retrieval performance using the ontology and the SWRL rules yielded an F-measure value of 94.8%. The NN had the highest accuracy in predicting Thai herb classes at 90.0% and the KNN had the lowest accuracy at 82.2%.
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