@Article{iasc.2020.010140,
AUTHOR = {Min Zhu, Huiyu Jin, Ruxue Chen, Quanyi Huang, Shaobo Zhong, Guang Tian},
TITLE = {Ontology-Supported Double-Level Model Construction for International  Disaster Medical Relief Resource Forecasting},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {5},
PAGES = {1097--1109},
URL = {http://www.techscience.com/iasc/v26n5/40828},
ISSN = {2326-005X},
ABSTRACT = {In a disaster, mass casualties lead to a surge in demand for medical 
services. Some relief actions have been criticized for being ill-adapted to 
dominating medical needs. This research established a disaster medical relief
planning model in 3 steps. 1. Establishing the two-level conceptual model. 
2. Using the ontology method to describe the hierarchy and relating rules of the 
terms and concepts associated with the model. 3. Using an ontology-support casebased reasoning approach to build the case similarity matching process, which can 
provide a more efficient system for decision support. A case study validated the 
model and demonstrated its usage.},
DOI = {10.32604/iasc.2020.010140}
}