Towards matching food metadata in emergency decision-making using ontology and MapReduce
Data integration technologies improve the accuracy of decision-making in the occurrence of public emergency. Set in the emergency food supply, we propose in this paper a new method to match food metadata with ontology and MapReduce. In specific, by extending the “ARGOVOC” ontology from the Food and Agriculture Organization of United Nations (FAO), ontological descriptions about food metadata are established. Based upon the classification of emergency functionalities, food metadata in the same category are matched in a two-stage TFIDF way, which is further implemented using the MapReduce framework for efficient parallel computation. Evaluations on the subsidiary agricultural product metadata, provided by the Suguo supermarket, show the feasibility of our approach.