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A Study on Ontology Structure Matching

机译：本体结构匹配研究

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In an E-Commerce environment with appeals in efficiency, the ontology constituted by Mobile Message and the time spent on conveying messages to appropriate users, will attract a high degree of attention. Nonetheless current study environment still lacks significant exploration in this field. In view of this, the study established mobile message template using Generally in the field of ontology, much emphasis is placed on how to apply ontology information; whereas few studies explore the efficacy of matching while more emphasize on how to match with respect to ontology matching. Under current mobile-commerce environment, the content of messages conveyed by shops mostly renders in texts. Consequently efficiency becomes the most important key to consideration. This article will conduct structural similarity matching through ontology structure, exploring the efficacies generated via different methods, and matching the two types of traditional ontology: Performance assessment was conducted using Breadth-First-Matching (BFM), Depth-First-Matching (DFM) and Node-Index-Matching (NIM) proposed by the study, in order to determine the most suitable method based on the numbers of matching. The experimental data showed that Node-Index-Matching (NIM) proposed by the study had significantly optimal performance on efficacy assessment, which consequently is more suitable for use in mobile environment with large quantity of messages.
机译：在效率颇高的电子商务环境中，由移动消息构成的本体以及将消息传递给适当的用户所花费的时间将引起高度关注。然而，当前的研究环境在该领域仍缺乏重大探索。有鉴于此，本研究使用本体建立了移动消息模板。通常，在本体领域中，重点是如何应用本体信息。很少有研究探索匹配的功效，而更多地着重于本体匹配方面的匹配方法。在当前的移动商务环境下，商店传达的消息内容大多以文本形式呈现。因此，效率成为考虑的最重要的关键。本文将通过本体结构进行结构相似性匹配，探索通过不同方法产生的效率，并匹配两种传统本体：使用广度优先匹配（BFM），深度优先匹配（DFM）进行性能评估。以及研究提出的节点索引匹配（NIM），以便根据匹配次数确定最合适的方法。实验数据表明，该研究提出的节点索引匹配（NIM）在效能评估上具有明显的最佳性能，因此更适合在消息量很大的移动环境中使用。

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