1. 医联网应用中异源健康数据语义融合方法研究.
- Author
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徐博艺, 金初阳, 胡代平, 张鹏翥, and 蔡鸿明
- Subjects
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LINKED data (Semantic Web) , *ACQUISITION of data , *MULTISENSOR data fusion , *INTERNET of things , *ALGORITHMS , *ONTOLOGIES (Information retrieval) - Abstract
Semantic heterogeneity exists in physiological index data collected by various sensors in medical Internet of Things and mobile medical applications, as well as in different kinds of healthcare/ medical data. This leads to difficulty of data fusion for smart medical IoT devices. In order to solve this problem, this paper developed semantic disambiguation method based on open linked data. First, the method modeled device data to from local ontology. Then it used graph-matching algorithm to align concepts between local ontology and open medical data source to indirectly eliminate semantic heterogeneity of multi-source data. Finally, by linking and matching to open linked data sources, the experiment of data fusion between sports bracelets and intelligent weight meter equipment determine that heterogeneous concepts, such as blood pressure and weight, are semantic related concepts. The results show that linking to open data sources may realize semantic extension of local ontology, and then realize data fusion of heterogeneous medical IoT devices. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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