02500nas a2200397   4500000000100000000000100001008004100002260001300043653002300056653002300079653001400102653003400116653001900150653001700169653002000186653001400206653002500220653002300245653002300268653002900291653002900320653003300349653003300382653002000415100001900435700001500454700001900469700001800488700001400506245010600520856015200626300001000778490001400788520125800802020004202060       2020                            d  bSpringer10aClinical diagnosis10aClinical knowledge10aDiagnosis10aIntangible cultural heritages10aSafety devices10asemantic web10aSemantic search10aSemantics10aTemporal information10aTemporal intention10aTemporal knowledge10aTemporal knowledge graph10aTemporal semantic search10aTraditional Chinese Medicine10aTraditional Chinese medicine10aUser experience1 aChengbiao Yang1 aWeizhuo Li1 aXiaoping Zhang1 aRunshun Zhang1 aGuilin Qi00aA Temporal Semantic Search System for Traditional Chinese Medicine Based on Temporal Knowledge Graphs  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85081181748&doi=10.1007%2f978-981-15-3412-6_2&partnerID=40&md5=fac33817943fc3fbf713e68fb6239807  a13-200 v1157 CCIS3 aTraditional 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.  a18650929 (ISSN); 9789811534119 (ISBN)