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.

中文翻译：

---

通过紧凑型协同萤火虫算法优化传感器本体对齐方式。

语义传感器Web（SSW）将语义Web技术与传感器网络联系在一起，后者利用传感器本体描述传感器信息。用不同的传感器本体注释传感器数据可能有助于实现不同传感器系统的互操作性，这要求传感器本体本身是可互操作的。因此，有必要通过在语义相关的传感器信息之间建立有意义的链接来匹配传感器的本体。由于Swarm智能算法（SIA）代表了一种解决本体匹配问题的好方法，因此我们研究了一种流行的SIA，即Firefly算法（FA），以优化本体的对齐方式。为了节省内存消耗并更好地权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型协同萤火虫算法（CcFA）的通用本体匹配技术，该技术将紧凑型编码机制与协同进化机制相结合。我们的建议使用格雷代码对解决方案进行编码，使用两个紧凑算子分别实施开发策略和探索策略，并使用两个概率矢量（PV）表示分别专注于开发和探索的群体。通过每一代中两个群体之间的通信，CcFA在解决传感器本体匹配问题时能够有效地提高搜索效率。实验利用会议轨迹和三对真实的传感器本体来测试我们提案的性能。