Integrating Sensor Ontologies with Global and Local Alignment Extractions
In order to enhance the communication between sensor networks in the Internet of things (IoT), it is indispensable to establish the semantic connections between sensor ontologies in this field. For this purpose, this paper proposes an up-and-coming sensor ontology integrating technique, which uses d...
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Series: | Wireless Communications and Mobile Computing |
Online Access: | http://dx.doi.org/10.1155/2021/6625184 |
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doaj-ae0e8d9b2f844621a01940ae4aebbf8a2021-02-22T00:01:29ZengHindawi-WileyWireless Communications and Mobile Computing1530-86772021-01-01202110.1155/2021/6625184Integrating Sensor Ontologies with Global and Local Alignment ExtractionsXingsi Xue0Xiaojing Wu1Chao Jiang2Guojun Mao3Hai Zhu4Fujian Provincial Key Laboratory of Big Data Mining and ApplicationsIntelligent Information Processing Research CenterIntelligent Information Processing Research CenterFujian Provincial Key Laboratory of Big Data Mining and ApplicationsSchool of Network EngineeringIn order to enhance the communication between sensor networks in the Internet of things (IoT), it is indispensable to establish the semantic connections between sensor ontologies in this field. For this purpose, this paper proposes an up-and-coming sensor ontology integrating technique, which uses debate mechanism (DM) to extract the sensor ontology alignment from various alignments determined by different matchers. In particular, we use the correctness factor of each matcher to determine a correspondence’s global factor, and utilize the support strength and disprove strength in the debating process to calculate its local factor. Through comprehensively considering these two factors, the judgment factor of an entity mapping can be obtained, which is further applied in extracting the final sensor ontology alignment. This work makes use of the bibliographic track provided by the Ontology Alignment Evaluation Initiative (OAEI) and five real sensor ontologies in the experiment to assess the performance of our method. The comparing results with the most advanced ontology matching techniques show the robustness and effectiveness of our approach.http://dx.doi.org/10.1155/2021/6625184 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xingsi Xue Xiaojing Wu Chao Jiang Guojun Mao Hai Zhu |
spellingShingle |
Xingsi Xue Xiaojing Wu Chao Jiang Guojun Mao Hai Zhu Integrating Sensor Ontologies with Global and Local Alignment Extractions Wireless Communications and Mobile Computing |
author_facet |
Xingsi Xue Xiaojing Wu Chao Jiang Guojun Mao Hai Zhu |
author_sort |
Xingsi Xue |
title |
Integrating Sensor Ontologies with Global and Local Alignment Extractions |
title_short |
Integrating Sensor Ontologies with Global and Local Alignment Extractions |
title_full |
Integrating Sensor Ontologies with Global and Local Alignment Extractions |
title_fullStr |
Integrating Sensor Ontologies with Global and Local Alignment Extractions |
title_full_unstemmed |
Integrating Sensor Ontologies with Global and Local Alignment Extractions |
title_sort |
integrating sensor ontologies with global and local alignment extractions |
publisher |
Hindawi-Wiley |
series |
Wireless Communications and Mobile Computing |
issn |
1530-8677 |
publishDate |
2021-01-01 |
description |
In order to enhance the communication between sensor networks in the Internet of things (IoT), it is indispensable to establish the semantic connections between sensor ontologies in this field. For this purpose, this paper proposes an up-and-coming sensor ontology integrating technique, which uses debate mechanism (DM) to extract the sensor ontology alignment from various alignments determined by different matchers. In particular, we use the correctness factor of each matcher to determine a correspondence’s global factor, and utilize the support strength and disprove strength in the debating process to calculate its local factor. Through comprehensively considering these two factors, the judgment factor of an entity mapping can be obtained, which is further applied in extracting the final sensor ontology alignment. This work makes use of the bibliographic track provided by the Ontology Alignment Evaluation Initiative (OAEI) and five real sensor ontologies in the experiment to assess the performance of our method. The comparing results with the most advanced ontology matching techniques show the robustness and effectiveness of our approach. |
url |
http://dx.doi.org/10.1155/2021/6625184 |
work_keys_str_mv |
AT xingsixue integratingsensorontologieswithglobalandlocalalignmentextractions AT xiaojingwu integratingsensorontologieswithglobalandlocalalignmentextractions AT chaojiang integratingsensorontologieswithglobalandlocalalignmentextractions AT guojunmao integratingsensorontologieswithglobalandlocalalignmentextractions AT haizhu integratingsensorontologieswithglobalandlocalalignmentextractions |
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1714853078927147008 |