【作者】 吴刚；

【导师】 胡亮；

【作者基本信息】 吉林大学 ， 计算机技术（专业学位）， 2020， 硕士

【摘要】 随着物联网的快速发展,联网物品的数量及其交互不断增加。诸如IPv6和5G之类的新兴通信技术的出现,提供了足够的网络地址并提高了数据传输的效率,从而进一步加速了联网物品的增长。海量物品之间的交互和交流产生了大量在语义上多种多样的上下文感知数据。将这些数据转化为知识并且表示为机器可理解的形式将有利于提升系统的扩展性和互操作性,从而促进物联网应用程序的开发和实现物联网数据的处理与融合。然而,传统的数据表示方法如语义标注、本体和语义网是基于规则的,当应用于物联网时缺乏灵活性和自适应性。为了应对这一挑战,本文主要聚焦于语义表示的问题,这对于物联网数据的处理和融合至关重要。为了充当桥梁,我们提出了一个高层框架,即Things2Vec,该框架旨在通过图表示学习技术从物品的交互中产生潜在的语义表示。这些语义表示使各种物联网语义分析任务受益,例如物联网服务推荐和物品的自动化。在Things2Vec中,我们利用图对由物品交互生成的函数序列关系进行建模,这被称为物联网上下文图。由于这些函数序列关系在语义上是异质的,因此会导致通用的图表示学习方法无法学习完整的信息。因此,我们提出了一种带偏置的随机游走程序,该程序专门针对捕获具有不同种类型语义关系的节点的邻居。为了验证所提出的框架的有效性,我们在真实世界物联网数据集IFTTT上引导了多标签分类实验。实验结果显示基于我们提出的框架,三种通用的图表示学习方法都展示了良好的表现。更进一步,与其他对比方法相比,仅带有20%标签节点的Things2Vec框架在Micro-F1方面实现了3%~13%的提升,在Macro-F1方面实现了3%~37%的提升。这证明提出的方法可以有效地捕获物联网中上下文感知数据之间的语义关系。更多还原

【Abstract】 With the quick development of the Internet of Things(IoT),the number of connected things and their interactions continue to increase.The advent of emerging communication technologies such as Internet Protocol Version 6(IPv6)and fifthgeneration(5G),provides sufficient network addresses and improves the efficiency of data transmission.These technologies further accelerate the growth of connected things.The interaction and communication among a large number of things generate an enormous amount of context-aware data that is semantically diverse.Transforming these data into knowledge and represent it as the machine-understandable form will help improve the scalability and interoperability of the IoT system,thereby promoting the development of IoT applications and the processing and fusion of IoT data.However,traditional data representation approaches such as semantic annotation,ontology,and semantic web technology are rule-based,which lack flexibility and adaptability when applied to IoT.To address the challenge,this paper mainly focuses on the problem of semantic representation,which is essential for processing and fusion of IoT data.To serve as a bridge,we propose a high-level framework,namely Things2 Vec,which aims to produce the latent semantic representations from the interaction of things though the graph representation learning technique.These semantic representations benefit various IoT semantic analysis tasks such as the IoT service recommendation and automation of things.In Things2 Vec,we utilize the graph to model the function sequence relationships that are generated by the interaction of things,which is called the IoT context graph.Since these function sequence relationships are heterogeneous in terms of semantics,it causes general graph representation learning methods to fail to learn complete information.Thus,we propose a biased random walk procedure,which is tailored to capture the neighborhoods of nodes with different types of semantic relationships.To demonstrate the effectiveness of the proposed framework,we conduct a multi-label classification experiment on the real-world IoT dataset IFTTT.Experimental results show that three general graph representation learning methods all show good performance based on our proposed framework.Furthermore,Things2 Vec with just 20% labeled nodes achieves 3%~13% improvements in terms of Micro-F1 and 3%~37% gains in terms of Macro-F1 over other compared methods.That proves that the proposed method can effectively capture the semantic relationship between contextaware data in IoT.更多还原