Fujian Key Lab for Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, China.
Guangxi Key Laboratory of Automatic Detecting Technology and Instruments (Guilin University of Electronic Technology), Guilin 541004, China.
Sensors (Basel). 2020 Apr 6;20(7):2056. doi: 10.3390/s20072056.
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.
语义传感器网络 (SSW) 将语义网技术与传感器网络联系起来，利用传感器本体论来描述传感器信息。使用不同的传感器本体论对传感器数据进行注释可以帮助实现不同传感器系统的互操作性，这要求传感器本体论本身是可互操作的。因此，有必要通过建立语义相关传感器信息之间的有意义链接来匹配传感器本体论。由于群智能算法 (SIA) 代表了解决本体匹配问题的一种很好的方法，我们研究了一种流行的 SIA，即萤火虫算法 (FA)，以优化本体对齐。为了节省内存消耗并更好地平衡算法的开发和探索，在这项工作中，我们提出了一种基于紧凑共萤火虫算法 (CcFA) 的通用本体匹配技术，该技术将紧凑编码机制与共同进化机制相结合。我们的方案利用格雷码对解进行编码，使用两个紧凑算子分别实现开发策略和探索策略，并使用两个概率向量 (PV) 分别表示专注于开发和探索的群体。通过在每一代中两个群体之间的通信，CcFA 能够有效地提高解决传感器本体匹配问题时的搜索效率。实验利用会议跟踪和三对真实传感器本体论来测试我们方案的性能。统计结果表明，基于 CcFA 的本体论匹配技术能够有效地匹配传感器本体论和会议领域的其他通用本体论。
