Abstract
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.
Highlights
In recent years, sensors have been used in a wide range of applications, such as urban traffic planning, flood prediction, health care, satellite imaging for earth and space observation et al To make different types of sensors collaborate on a common task to detect and identify a multitude of observations, we need to combine the sensor data with the Internet, web services and database technologies, which is the so-called sensor web [1,2]
We combines the Compact Optimization Algorithm (COA) with co-Evolutionary Algorithm (cEA) to have their complementary advantages, and being inspired by the success of Firefly Algorithm (FA) in many domains [17,18], we further propose a general-purpose ontology matching technique based on Compact co-Firefly Algorithm (CcFA) to efficiently optimize the ontology alignments
The rest of the paper is organized as follows: Section 3 presents the basic concepts on sensor ontology matching; Section 4 shows the CcFA-based sensor ontology matching technique in details; Section 5 presents the greedy strategy to filtering the final alignment; Section 6 shows the statistical experiment; and Section 7 draws the conclusion and presents the future work
Summary
Sensors have been used in a wide range of applications, such as urban traffic planning, flood prediction, health care, satellite imaging for earth and space observation et al To make different types of sensors collaborate on a common task to detect and identify a multitude of observations, we need to combine the sensor data with the Internet, web services and database technologies, which is the so-called sensor web [1,2]. Modeling two sensor ontologies under alignment is a complex and time-consuming task, when the scale of their entities is large For this reason, approximate methods, such as Swarm Intelligent Algorithm (SIA), represents a suitable methodology for determining the high-quality alignments [6]. To overcome the algorithm’s premature convergence, we investigate another kind of optimization algorithm, that is, co-Evolutionary Algorithm (cEA), which makes multiple sub-swarms evolves independently and exchanges the information of each sub-swarms at particular time to improve the searching efficiency in the large search space. We combines the COA with cEA to have their complementary advantages, and being inspired by the success of Firefly Algorithm (FA) in many domains [17,18], we further propose a general-purpose ontology matching technique based on Compact co-Firefly Algorithm (CcFA) to efficiently optimize the ontology alignments. The rest of the paper is organized as follows: Section 3 presents the basic concepts on sensor ontology matching; Section 4 shows the CcFA-based sensor ontology matching technique in details; Section 5 presents the greedy strategy to filtering the final alignment; Section 6 shows the statistical experiment; and Section 7 draws the conclusion and presents the future work
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