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- W2123232065 abstract "Unlike Western medicine, traditional Chinese medicine (TCM) is based on inherent rules or patterns, which can be considered as causal links. Existing approaches tend to apply computational methods to semantic ontologies to do knowledge mining, but they cannot perfectly make use of the internal principles in TCM. The principal objective of TCM knowledge modeling is to figure out a formal method to represent Chinese medicine knowledge. When it comes to knowledge representation, we can transform this inherent knowledge into causal graphs. In this chapter, we present an approach to building a TCM knowledge model with the capability of rule reasoning using OWL 2. In particular, we focused on the causal relations between syndrome and symptoms and changes between syndromes. In this chapter, the method of causal knowledge reasoning integrates defined ontology with predefined rules to compose a causal graph, which can clearly demonstrate the process of TCM diagnosis and show the potential to do knowledge mining. We evaluated our approach by showing 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 great potential in TCM knowledge mining." @default.
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- W2123232065 date "2012-01-01" @default.
- W2123232065 modified "2023-09-26" @default.
- W2123232065 title "Causal Knowledge Modeling for Traditional Chinese Medicine Using OWL 2" @default.
- W2123232065 cites W2010411374 @default.
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- W2123232065 doi "https://doi.org/10.1016/b978-0-12-398510-1.00008-x" @default.
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