Optimizing Sensor Ontology Alignment through Compact co-Firefly Algorithm
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Fujian Key Lab for Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, China
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Guangxi Key Laboratory of Automatic Detecting Technology and Instruments (Guilin University of Electronic Technology), Guilin 541004, China
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Intelligent Information Processing Research Center, Fujian University of Technology, Fuzhou 350118, China
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Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fuzhou 350118, China
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College of Information Science and Engineering, Fujian University of Technology, Fuzhou 350118, China
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College of IOT Engineering, Hohai University, Changzhou 213022, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(7), 2056; https://doi.org/10.3390/s20072056
Received: 17 February 2020
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Revised: 2 April 2020
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Accepted: 2 April 2020
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Published: 6 April 2020
(This article belongs to the Special Issue Machine Learning and Intelligent Optimization Data Aggregation in Internet of Things)
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.
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MDPI and ACS Style
Xue, X.; Chen, J. Optimizing Sensor Ontology Alignment through Compact co-Firefly Algorithm. Sensors 2020, 20, 2056. https://doi.org/10.3390/s20072056
AMA Style
Xue X, Chen J. Optimizing Sensor Ontology Alignment through Compact co-Firefly Algorithm. Sensors. 2020; 20(7):2056. https://doi.org/10.3390/s20072056
Chicago/Turabian StyleXue, Xingsi, and Junfeng Chen. 2020. "Optimizing Sensor Ontology Alignment through Compact co-Firefly Algorithm" Sensors 20, no. 7: 2056. https://doi.org/10.3390/s20072056
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