Optimizing Sensor Ontology Alignment through Compact co-Firefly Algorithm
Semantic Sensor Web (SSW) links the semantic web technique with the sensor network, which utilizes sensor ontology to describe sensor information. Annotating sensor data with different sensor ontologies can be of help to implement different sensor systems’ inter-operability, which requires that the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/7/2056 |
| _version_ | 1797571310280245248 |
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| author | Xingsi Xue Junfeng Chen |
| author_facet | Xingsi Xue Junfeng Chen |
| author_sort | Xingsi Xue |
| collection | DOAJ |
| description | Semantic Sensor Web (SSW) links the semantic web technique with the sensor network, which utilizes sensor ontology to describe sensor information. Annotating sensor data with different sensor ontologies can be of help to implement different sensor systems’ inter-operability, which requires that the sensor ontologies themselves are inter-operable. Therefore, it is necessary to match the sensor ontologies by establishing the meaningful links between semantically related sensor information. Since the Swarm Intelligent Algorithm (SIA) represents a good methodology for addressing the ontology matching problem, we investigate a popular SIA, that is, the Firefly Algorithm (FA), to optimize the ontology alignment. To save the memory consumption and better trade off the algorithm’s exploitation and exploration, in this work, we propose a general-purpose ontology matching technique based on Compact co-Firefly Algorithm (CcFA), which combines the compact encoding mechanism with the co-Evolutionary mechanism. Our proposal utilizes the Gray code to encode the solutions, two compact operators to respectively implement the exploiting strategy and exploring strategy, and two Probability Vectors (PVs) to represent the swarms that respectively focuses on the exploitation and exploration. Through the communications between two swarms in each generation, CcFA is able to efficiently improve the searching efficiency when addressing the sensor ontology matching problem. The experiment utilizes the Conference track and three pairs of real sensor ontologies to test our proposal’s performance. The statistical results show that CcFA based ontology matching technique can effectively match the sensor ontologies and other general ontologies in the domain of organizing conferences. |
| first_indexed | 2024-03-10T20:38:28Z |
| format | Article |
| id | doaj.art-1f82a41157254ba79e5539229b9220c1 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T20:38:28Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-1f82a41157254ba79e5539229b9220c12023-11-19T20:49:30ZengMDPI AGSensors1424-82202020-04-01207205610.3390/s20072056Optimizing Sensor Ontology Alignment through Compact co-Firefly AlgorithmXingsi Xue0Junfeng Chen1Fujian Key Lab for Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, ChinaCollege of IOT Engineering, Hohai University, Changzhou 213022, ChinaSemantic Sensor Web (SSW) links the semantic web technique with the sensor network, which utilizes sensor ontology to describe sensor information. Annotating sensor data with different sensor ontologies can be of help to implement different sensor systems’ inter-operability, which requires that the sensor ontologies themselves are inter-operable. Therefore, it is necessary to match the sensor ontologies by establishing the meaningful links between semantically related sensor information. Since the Swarm Intelligent Algorithm (SIA) represents a good methodology for addressing the ontology matching problem, we investigate a popular SIA, that is, the Firefly Algorithm (FA), to optimize the ontology alignment. To save the memory consumption and better trade off the algorithm’s exploitation and exploration, in this work, we propose a general-purpose ontology matching technique based on Compact co-Firefly Algorithm (CcFA), which combines the compact encoding mechanism with the co-Evolutionary mechanism. Our proposal utilizes the Gray code to encode the solutions, two compact operators to respectively implement the exploiting strategy and exploring strategy, and two Probability Vectors (PVs) to represent the swarms that respectively focuses on the exploitation and exploration. Through the communications between two swarms in each generation, CcFA is able to efficiently improve the searching efficiency when addressing the sensor ontology matching problem. The experiment utilizes the Conference track and three pairs of real sensor ontologies to test our proposal’s performance. The statistical results show that CcFA based ontology matching technique can effectively match the sensor ontologies and other general ontologies in the domain of organizing conferences.https://www.mdpi.com/1424-8220/20/7/2056sensor ontologyCompact co-Firefly Algorithmontology matching |
| spellingShingle | Xingsi Xue Junfeng Chen Optimizing Sensor Ontology Alignment through Compact co-Firefly Algorithm Sensors sensor ontology Compact co-Firefly Algorithm ontology matching |
| title | Optimizing Sensor Ontology Alignment through Compact co-Firefly Algorithm |
| title_full | Optimizing Sensor Ontology Alignment through Compact co-Firefly Algorithm |
| title_fullStr | Optimizing Sensor Ontology Alignment through Compact co-Firefly Algorithm |
| title_full_unstemmed | Optimizing Sensor Ontology Alignment through Compact co-Firefly Algorithm |
| title_short | Optimizing Sensor Ontology Alignment through Compact co-Firefly Algorithm |
| title_sort | optimizing sensor ontology alignment through compact co firefly algorithm |
| topic | sensor ontology Compact co-Firefly Algorithm ontology matching |
| url | https://www.mdpi.com/1424-8220/20/7/2056 |
| work_keys_str_mv | AT xingsixue optimizingsensorontologyalignmentthroughcompactcofireflyalgorithm AT junfengchen optimizingsensorontologyalignmentthroughcompactcofireflyalgorithm |