Abstract
Traditional Chinese medicine (TCM) is an important intangible cultural heritage of China. To enhance the services of TCM, many works focus on constructing various types of TCM knowledge graphs according to the concrete requirements such as information retrieval. However, most of them ignored several key issues. One is temporal information that is very important for TCM clinical diagnosis and treatment. For example, a herb needs to be boiled for different periods in different prescriptions, but existing methods cannot represent this temporal information very well. The other is that current TCM-based retrieval systems cannot effectively deal with the temporal intentions of search sentences, which leads to bad experiences for users in retrieval services. To solve these issues, we propose a new model tailored for TCM based on the temporal knowledge graph in this paper, which can effectively represent the clinical knowledge changing dynamically over time. Moreover, we implement a temporal semantic search system and employ reasoning rules based on our proposed model to complete the temporal intentions of search sentences. The preliminary result indicates that our system can obtain better results than existing methods in terms of precision.
This work was partially supported by the National Key R&D Program of China under grant (2017YFB1002302), the NSFC grant (U1736204), the Fundamental Research Funds for the Central public welfare research institutes (ZZ11-064).
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Yang, C., Li, W., Zhang, X., Zhang, R., Qi, G. (2020). A Temporal Semantic Search System for Traditional Chinese Medicine Based on Temporal Knowledge Graphs. In: Wang, X., Lisi, F., Xiao, G., Botoeva, E. (eds) Semantic Technology. JIST 2019. Communications in Computer and Information Science, vol 1157. Springer, Singapore. https://doi.org/10.1007/978-981-15-3412-6_2
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