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Ontology-based metadata matching for emergency decision-making using MapReduce

Ontology matching techniques can help improve the accuracy of emergency decision-making on heterogeneous data. In this paper, we propose a practical approach to leverage ontology and MapReduce for matching metadata of emergency supplies. We use this approach in the domain of emergency food. Specifically, by extending the AGROVOC ontology developed by the Food and Agriculture Organisation FAO of United Nations, ontological descriptions of food metadata are established. Then, based on the classification of emergency functionality, the instances of food metadata within the same category are matched in a two-stage TF-IDF fashion, which is further improved with the MapReduce framework for efficient parallel computation. Our experiments on grain, meat and milk metadata retrieved from several e-commerce websites demonstrate the good performance of the proposed approach. Additionally, case study is provided to illustrate the potential use of our approach.