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In this paper, a novel medical knowledge graph in Chinese approach applied in smart healthcare based on IoT and WoT is presented, using deep neural networks combined with self-attention to generate medical knowledge graph to make it more convenient for performing disease diagnosis and providing treatment advisement. Although great success has been...
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Heterogeneous access is an issue caused by different standards of devices and systems in the Internet of Things(IoT), which makes cross-domain devices difficult to connect and communicate with each other. In this paper, to solve the interoperability problem for cross-domain IoT, a multi-layer semantic middleware framework using a WoT-based knowledg...
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... In contrast, this work can reduce the influence of such incompleteness and improve the stability of recommendation systems. Wanheng Liu proposed a framework based on deep neural networks, which can be used to generate Chinese Knowledge Graph [10]. This model fills the gap in Chinese Medical Knowledge Graph research, which is better than previous models such as conditional random field (CRF) and BiLSTM-CNNs in terms of accuracy. ...
With the development of Artificial Intelligence, Knowledge Graph has become a popular direction in recent years, with which many practical problems have been solved. As a special type of Knowledge Graph, Medical Knowledge Graph is an important research field. While most of related research describes the construction and application of Medical Knowledge Graph (MKG) for a certain disease, there lacks reviews on previous research on Medical Knowledge Graph in recent years. In order to investigate recent research of Medical Knowledge Graph, this paper reviews studies of the construction and application of Medical Knowledge Graph in recent years, dividing them into six parts according to the content. Subsequently, some suggestions are put forward for the development of Knowledge Graph.
... From another point of view, the wide demand for knowledge graphs in these fields also promotes the continuous improvement of knowledge graph technology. For example, considering insufficient data and difficulty in knowledge updating, Liu et al. [7] proposed a new Chinese healthcare knowledge graph based on deep learning methods which can be applied to mobile devices based on IoT (Internet of Things) and WoT (Web of Things). In order to solve the problem of insufficient data, 600 thousand pieces of data were processed to support the training of the model and a good performance was achieved. ...
... There are many problems and challenges [7] in the use and maintenance of the base station information table in network operation and maintenance. The base station information table is usually stored, managed, and presented in the form of a simple relational database, which needs manual summary input and update and is of a quasi-static data form. ...
... Based on massive wireless network perception data, the automatic extraction of relevant information and the construction of wireless network knowledge graphs [7] is a more comprehensive, timely, dynamic, and visual method of base station information storage management and presentation, which is conducive to improve the intelligence level of mobile network operations and maintenance and improve the efficiency of operation and maintenance work. If the data source contains the network sampling data of other operators, the wireless network information can be effectively extracted and appropriately presented in the wireless network knowledge graph so as to facilitate cross-network benchmarking and network operation and maintenance based on the comparative advantage. ...
In recent years, with the rapid development of Internet technology and applications, the scale of Internet data has exploded, which contains a significant amount of valuable knowledge. The best methods for the organization, expression, calculation, and deep analysis of this knowledge have attracted a great deal of attention. The knowledge graph has emerged as a rich and intuitive way to express knowledge. Knowledge reasoning based on knowledge graphs is one of the current research hot spots in knowledge graphs and has played an important role in wireless communication networks, intelligent question answering, and other applications. Knowledge graph-oriented knowledge reasoning aims to deduce new knowledge or identify wrong knowledge from existing knowledge. Different from traditional knowledge reasoning, knowledge reasoning methods oriented to knowledge graphs are more diversified due to the concise, intuitive, flexible, and rich knowledge expression forms in knowledge graphs. Based on the basic concepts of knowledge graphs and knowledge graph reasoning, this paper introduces the latest research progress in knowledge graph-oriented knowledge reasoning methods in recent years. Specifically, according to different reasoning methods, knowledge graph reasoning includes rule-based reasoning, distributed representation-based reasoning, neural network-based reasoning, and mixed reasoning. These methods are summarized in detail, and the future research directions and prospects of knowledge reasoning based on knowledge graphs are discussed and prospected.
... This method can better analyze and understand the semantics of user requirements. Liu et al. [44] have proposed a Chinese medical knowledge graph method based on IOT and Web of Things concepts. This method uses a deep neural network combined with self-attention to generate a knowledge graph. ...
In the era of big data, mass customization (MC) systems are faced with the complexities associated with information explosion and management control. Thus, it has become necessary to integrate the mass customization system and Social Internet of Things, in order to effectively connecting customers with enterprises. We should not only allow customers to participate in MC production throughout the whole process, but also allow enterprises to control all links throughout the whole information system. To gain a better understanding, this paper first describes the architecture of the proposed system from organizational and technological perspectives. Then, based on the nature of the Social Internet of Things, the main technological application of the mass customization–Social Internet of Things (MC–SIOT) system is introduced in detail. On this basis, the key problems faced by the mass customization–Social Internet of Things system are listed. Our findings are as follows: (1) MC–SIOT can realize convenient information queries and clearly understand the user’s intentions; (2) the system can predict the changing relationships among different technical fields and help enterprise R&D personnel to find technical knowledge; and (3) it can interconnect deep learning technology and digital twin technology to better maintain the operational state of the system. However, there exist some challenges relating to data management, knowledge discovery, and human–computer interaction, such as data quality management, few data samples, a lack of dynamic learning, labor consumption, and task scheduling. Therefore, we put forward possible improvements to be assessed, as well as privacy issues and emotional interactions to be further discussed, in future research. Finally, we illustrate the behavior and evolutionary mechanism of this system, both qualitatively and quantitatively. This provides some idea of how to address the current issues pertaining to mass customization systems.
The novel IoT-based data sensing and service mode promotes the booming development of crowdsensing-based Mobile Communication Services (MCS). MCS facilitates people’s daily lives by providing appropriate services according to the user’s mobile travels. These travelling trajectories, combined with open-source network information, reveal multi-modal semantic information implicit in user mobility. Mining these mobile semantics contributes to understanding user mobility more sufficiently. It covers a wide spectrum of applications in mobile scenarios. For service providers, it improves the quality of their services. For mobile users, it helps to design more rigorous privacy-preserving mechanism. For third-party platforms, such mobility analysis enhances their data management, analysis and reusage. It has always been an open research issue in mobile computing. We are motivated to conduct a complete and comprehensive survey on semantic mining within the scope of MCS, forming a complete overview of mobile semantic perception. Specifically, we first review existing research works on feature selection. We classify them into five categories, depending on their representation forms. Then, we summarize the researches on mobile semantic perception and cluster them to be three groups according to the digging depth of the represented semantics. To complete the overview, we also review the applications of learning algorithms and discuss open opportunities and challenges for future works.
