Ontology-based metadata matching for emergency decision-making using MapReduce
Ontology-based metadata matching for emergency decision-making using MapReduce
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DOI：
10.1504/ijmso.2015.070821
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发表时间：
2015-07
期刊：
影响因子：
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通讯作者：
Li Zhu;Wei Hu
中科院分区：
文献类型：
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作者：
Li Zhu;Wei Hu
本体匹配技术有助于提高异构数据下应急决策的准确性。在本文中，我们提出了一个实用的方法来利用本体和MapReduce的应急物资的元数据匹配。我们在紧急食品领域采用这种方法。具体来说，通过扩展联合国粮农组织开发的AGROVOC本体，建立了食品元数据的本体描述。然后，基于应急功能的分类，采用两阶段TF-IDF方法对同一类别的食物元数据实例进行匹配，并在MapReduce框架下进行了进一步的改进，以实现高效的并行计算。我们的实验粮食，肉类和牛奶元数据检索从几个电子商务网站上展示了良好的性能所提出的方法。此外，案例研究来说明我们的方法的潜在用途。
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.