一個運用動態物聯網感測資料的語意標記方法

Abstract

在物聯網 (Internet of Things) 環境之下，感知器不停地蒐集資料。然而如何將這些資料轉換成機器可讀取且可讀懂的形式是一個困難的問題。本研究提出了一個語意標記方法，在感測資料上標記語意。首先，先建立一個作為基礎的原始本體論，再以K-Means分群法取出輸入資料中的新知識，並將此知識更新至原始本體論中，更新後的本體論將作為語意標記的根據。實驗結果顯示，本研究利用我們提出的方法，針對一個月的資料逐周分析，發現可以從輸入的資料中取出有用的知識，因此，我們可以在感測資料上標記更多知識。

In a dynamic Internet of Things (IoT) environment, sensors are used to continually collect data. However, it is difficult to transform those data into a machine-readable and machine-interpretable form. In this paper, we propose a semantic annotation approach to annotate sensor data via semantics. First, a base ontology is built. Then, new knowledge is collected from input data by using the K-Means clustering, and updated into the base ontology. The updated ontology forms the basis for semantic annotation. The simulation results show that we analysis the data for one month period week by week using the proposed approach is able to find useful knowledge out of the new input data. Therefore, we can annotate sensor data with more knowledge.