Dynamic Data Driven-based Automatic Clustering and Semantic Annotation for Internet of Things Sensor Data

Lin, Szu-Yin; Li, Junbin; Yu, Ching-Tzu

doi:10.18494/sam.2019.2333

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…Quantity % Semi-automated [58], [50], [83], [18], [84], [31], [89], [86], [15], [16], [42], [44], [64], [47], [82], [74], [55], [21], [8], [7], [43], [28], [53] 23 57…”

Section: Degree Of Automation Studiesunclassified

Automated Semantic Annotation Deploying Machine Learning Approaches: A Systematic Review

Chang,

Sangodiah

2023

mendel

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Semantic Web is the vision to make Internet data machine-readable to achieve information retrieval with higher granularity and personalisation. Semantic annotation is the process that binds machine-understandable descriptions into Web resources such as text and images. Hence, the success of Semantic Web dependson the wide availability of semantically annotated Web resources. However, there remains a huge amount of unannotated Web resources due to the limited annotation capability available. In order to address this, machine learning approaches have been used to improve the automation process. This Systematic Review aims to summarise the existing state-of-the-art literature to answer five Research Questions focusing on machine learning driven semantic annotation automation. The analysis of 40 selected primary studies reveals that the use of unitary and combination of machine learning algorithms are both the current directions. SupportVector Machine (SVM) is the most-used algorithm, and supervised learning is the predominant machine learning type. Both semi-automated and fully automated annotation are almost nearly achieved. Meanwhile, text is the most annotated Web resource; and the availability of third-party annotation tools is in-line with this. While Precision, Recall, F-Measure and Accuracy are the most deployed quality metrics, not all the studies measured the quality of the annotated results. In the future, standardising quality measures is the direction for research.

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“…Quantity % Semi-automated [58], [50], [83], [18], [84], [31], [89], [86], [15], [16], [42], [44], [64], [47], [82], [74], [55], [21], [8], [7], [43], [28], [53] 23 57…”

Section: Degree Of Automation Studiesunclassified

Automated Semantic Annotation Deploying Machine Learning Approaches: A Systematic Review

Chang,

Sangodiah

2023

mendel

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“…Rida et al [24] utilized data aggregation techniques based on the Euclidean distance to reduce similar data. Lin et al [25] proposed a semantic data annotation method based on semantics. A data clustering method, which groups homogeneous data into clusters and then performs data reduction by selecting the average value of each cluster, was proposed based on histograms for data reduction [26].…”

Section: Related Workmentioning

confidence: 99%

Edge Sensing‐Enabled Multistage Hierarchical Clustering Deredundancy Algorithm in WSNs

Zhu

et al. 2021

Wireless Communications and Mobile Computing

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Due to the defects caused by limited energy, storage capacity, and computing ability, the increasing amount of sensing data has become a challenge in wireless sensor networks (WSNs). To decrease the additional power consumption and extend the lifetime of a WSN, a multistage hierarchical clustering deredundancy algorithm is proposed. In the first stage, a dual-metric distance is employed, and redundant nodes are preliminarily identified by the improved k -means algorithm to obtain clusters of similar nodes. Then, a Gaussian hybrid clustering classification algorithm is presented to implement data similarity clustering for edge sensing data in the second stage. In the third stage, the clustered sensing data is randomly weighted to deduplicate the spatial correlation data. Detailed experimental results show that, compared with the existing schemes, the proposed deredundancy algorithm can achieve better performance in terms of redundant data ratio, energy consumption, and network lifetime.

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“…(31) The semantic sensor network ontology was also used to extract new knowledge through K-means clustering from dynamic IoT data. (32) However, the current integrating methods cannot be used to organize the knowledge obtained from conversations with chatbots.…”

Section: Related Workmentioning

confidence: 99%

Ontology-based Integration of Knowledge Base for Building an Intelligent Searching Chatbot

Nguyen¹,

Tran²,

Pham³

et al. 2021

Sensors and Materials

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A chatbot is a useful tool for communicating with users to extract necessary information. An intelligent chatbot requires an effective knowledge base as materials to adequately organize the knowledge domain and process many kinds of inputted queries as natural text. Ontology technology is effective for use in learning technology systems. In this paper, a model that uses ontology technology for relational knowledge to integrate a structure of scripts is presented. This integrated model, called the Rela-Scripts model, is used to organize the knowledge material of a chatbot to search for knowledge in education. Some problems in searching for knowledge by this chatbot are proposed and solved on the basis of the Rela-Scripts model. The proposed method is applied to build an intelligent chatbot for answering questions on contents of the Introduction to Programming course in a university. This chatbot acts as a tutor by communicating with students in Vietnamese and gives explanations that meet the requirements of students. Its instructions are useful for their self-learning to enhance their programming skills.

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Dynamic Data Driven-based Automatic Clustering and Semantic Annotation for Internet of Things Sensor Data

Cited by 3 publications

References 15 publications

Automated Semantic Annotation Deploying Machine Learning Approaches: A Systematic Review

Automated Semantic Annotation Deploying Machine Learning Approaches: A Systematic Review

Edge Sensing‐Enabled Multistage Hierarchical Clustering Deredundancy Algorithm in WSNs

Ontology-based Integration of Knowledge Base for Building an Intelligent Searching Chatbot

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