A Knowledge Annotation and Reasoning Semantic Web of Things Framework for Intelligent Diagnosis

被引：0
|
作者：
Yang Y.-N. [1 ]
Wu Z.-Y. [1 ]
Zhu X.-N. [1 ]
机构：
[1] School of Information and Communication, Beijing University of Posts and Telecommunications, Beijing
来源：
Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications | 2017年 / 40卷 / 04期
关键词：
Anomaly diagnosis; Entity linking; Semantic rules and reasoning; WoT knowledge base;
D O I：
10.13190/j.jbupt.2017.04.017
中图分类号：
学科分类号：
摘要：
The web of things(WoT) is one of the present advancements in which devices, objects, and sensors are getting linked to the semantic web. To solve the integration problems in developing WoT application with huge device data. A semantic annotation and reasoning based framework and approach was proposed. It presents a universal ontology for WoT applications with expanding a physical process model on the basis of reusing the traditional device description ontology. The framework also shows that semantic sensor networks, semantic web technologies, as well as reasoning mechanisms can help in real-world applications to automatically derive complex models for analytics tasks such as anomaly diagnosis and automatic control. The article demonstrates approaches for building automation system with numerous connected devices in knowledge base constructing and show how the semantic framework allows us to develop a complex WoT application. © 2017, Editorial Department of Journal of Beijing University of Posts and Telecommunications. All right reserved.
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页码：104 / 110
页数：6
相关论文
共 10 条
  • [1] Ploennigs J., Hensel B., Dibowski H., Et al., BASont-a modular, adaptive building automation system ontology, 38th Annual Conference on IEEE Industrial Electronics Society, pp. 4827-4833, (2012)
  • [2] Zhou Q., Wang S., Ma Z., A model-based fault detection and diagnosis strategy for HVAC systems, International Journal of Energy Research, 33, 10, pp. 903-918, (2009)
  • [3] Katipamula S., Brambley M., Methods for fault detection, diagnostics, and prognostics for building systems-a review, HVAC&R Research, 11, 1, pp. 3-25, (2005)
  • [4] Mulwad V., Finin T., Joshi A., Semantic message passing for generating linked data from tables, International Semantic Web Conference, pp. 363-378, (2013)
  • [5] Limaye G., Sarawagi S., Chakrabarti S., Annotating and searching web tables using entities, types and relationships, Proceedings of the VLDB Endowment, 3, 1-2, pp. 1338-1347, (2010)
  • [6] Seydoux N., Drira K., Hernandez N., Et al., IoT-O, a coredomain IoT ontology to represent connected devices net-works, Knowledge Engineering and Knowledge Management, pp. 561-576, (2016)
  • [7] Eberle J., Wijaya T.K., Aberer K., Online unsupervised state recognition in sensor data, 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 29-36, (2015)
  • [8] Wang P., Wang H., Wang W., Finding semantics in time series, 2011 ACM SIGMOD International Conference on Management of Data (SIGMOD'11), pp. 385-396, (2011)
  • [9] Ordonez F.J., Roggen D., Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition, Sensors, 16, (2016)
  • [10] Yang Y., Wu Z., Zhu X., Semi-automatic metadata annotation of web of things with knowledge base, IEEE International Conference on Network Infrastructure and Digital Content, pp. 124-129, (2016)
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