Towards a Semantic Web of Things: A Hybrid Semantic Annotation, Extraction, and Reasoning Framework for Cyber-Physical System

Wu, Zhenyu; Xu, Yuan; Yang, Yunong; Zhang, Chunhong; Zhu, Xinning; Ji, Yang

doi:10.3390/s17020403

Cited by 38 publications

(27 citation statements)

References 30 publications

(41 reference statements)

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“…In such distributed and heterogeneous systems, knowledge sharing can be a problem. Therefore, CPS also uses semantics to represent the structure of the shared knowledge, thereby allowing distributed agents to understand themselves during the cooperation processes [ 26 , 32 , 33 ]. CPS needs to deal with the problem posed by heterogeneous networks and protocols when integrating components of different natures from different sources.…”

Section: Related Workmentioning

confidence: 99%

SLAE–CPS: Smart Lean Automation Engine Enabled by Cyber-Physical Systems Technologies

Wang

Zhao

2017

Sensors

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In the context of Industry 4.0, the demand for the mass production of highly customized products will lead to complex products and an increasing demand for production system flexibility. Simply implementing lean production-based human-centered production or high automation to improve system flexibility is insufficient. Currently, lean automation (Jidoka) that utilizes cyber-physical systems (CPS) is considered a cost-efficient and effective approach for improving system flexibility under shrinking global economic conditions. Therefore, a smart lean automation engine enabled by CPS technologies (SLAE–CPS), which is based on an analysis of Jidoka functions and the smart capacity of CPS technologies, is proposed in this study to provide an integrated and standardized approach to design and implement a CPS-based smart Jidoka system. A set of comprehensive architecture and standardized key technologies should be presented to achieve the above-mentioned goal. Therefore, a distributed architecture that joins service-oriented architecture, agent, function block (FB), cloud, and Internet of things is proposed to support the flexible configuration, deployment, and performance of SLAE–CPS. Then, several standardized key techniques are proposed under this architecture. The first one is for converting heterogeneous physical data into uniform services for subsequent abnormality analysis and detection. The second one is a set of Jidoka scene rules, which is abstracted based on the analysis of the operator, machine, material, quality, and other factors in different time dimensions. These Jidoka rules can support executive FBs in performing different Jidoka functions. Finally, supported by the integrated and standardized approach of our proposed engine, a case study is conducted to verify the current research results. The proposed SLAE–CPS can serve as an important reference value for combining the benefits of innovative technology and proper methodology.

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Section: Related Workmentioning

confidence: 99%

SLAE–CPS: Smart Lean Automation Engine Enabled by Cyber-Physical Systems Technologies

Wang

Zhao

2017

Sensors

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“…(iii) High-level queries enrich query results from cloud sources like the Linked Open Data cloud (https://lodcloud.net/) resulting in the Semantic Web of Things (SWoT) [42]. The two main technologies of the highlevel queries are the semantic queries based on the variants of the SPARQL [36] and Ontology-Based Data Access (OBDA) [148].…”

Section: The Application Layermentioning

confidence: 99%

“…The sensor, device, network (we refer as the gateway), service and application support, and application layers of IoT systems are shown in Figure 1. The enabling technologies of these layers like communication standards [1,40], protocols [40,41], high-level reasoning [42], and linked open data enrichment possibilities [43] are developing at a fast pace and crying out for semantic model-based standardization which will be overviewed in this paper.…”

Section: Introductionmentioning

confidence: 99%

A Review of Semantic Sensor Technologies in Internet of Things Architectures

Honti

Abonyi

2019

Complexity

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Intelligent sensors should be seamlessly, securely, and trustworthy interconnected to enable automated high-level smart applications. Semantic metadata can provide contextual information to support the accessibility of these features, making it easier for machines and humans to process the sensory data and achieve interoperability. The unique overview of sensor ontologies according to the semantic needs of the layers of IoT solutions can serve a guideline of engineers and researchers interested in the development of intelligent sensor-based solutions. The explored trends show that ontologies will play an even more essential role in interlinked IoT systems as interoperability and the generation of controlled linkable data sources should be based on semantically enriched sensory data.

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“…For example, thesauri can be applied for matching terms of different nomenclatures and therefore ease the combination of distinct databases [62]. More complex approaches have then to be utilized for automatic reasoning, consistency checks and finally for the generation of new hypotheses [82].…”

Section: Semantic Enrichmentmentioning

confidence: 99%

Big data and precision medicine: challenges and strategies with healthcare data

Kraus

Lausser

Kühn

et al. 2018

Int J Data Sci Anal

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Recent snapshots of the European progress on big data in health care and precision medicine reveal diverse perceptions of experts and the public, leading to the impression that algorithmic issues have the largest share among the challenges all health systems are faced with. Yet, from a comparison of different countries it is evident that the adaption and integration of heterogeneous data sources have a major impact on the advancement of precision medicine. Legal regulations for implementation and operation of healthcare networking are actively discussed in the public and gradually implemented in several countries. Based on a unified documentation, they are a perfect precondition for integrating distributed healthcare data to a big data platform with a reliable fact representation. Now, basic and clinical scientists have to be motivated to share their work with these data platforms. In this work, we aim to provide an overview on the common issues in big healthcare data applications and address the challenges for the involved scientific, clinical and administrative partners. We propose a possible strategy for a comprehensive data integration by iterating data harmonization, semantic enrichment and data analysis processes.

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Towards a Semantic Web of Things: A Hybrid Semantic Annotation, Extraction, and Reasoning Framework for Cyber-Physical System

Cited by 38 publications

References 30 publications

SLAE–CPS: Smart Lean Automation Engine Enabled by Cyber-Physical Systems Technologies

SLAE–CPS: Smart Lean Automation Engine Enabled by Cyber-Physical Systems Technologies

A Review of Semantic Sensor Technologies in Internet of Things Architectures

Big data and precision medicine: challenges and strategies with healthcare data

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