Abstract
In order to optimize intelligent applications driven by various sensors, it is vital to properly interpret and reuse sensor data from different domains. The construction of semantic maps which illustrate the relationship between heterogeneous domain ontologies plays an important role in knowledge reuse. However, most mapping methods in the literature use the literal meaning of each concept and instance in the ontology to obtain semantic similarity. This is especially the case for domain ontologies which are built for applications with sensor data. At the instance level, there is seldom work to utilize data of the sensor instances when constructing the ontologies’ mapping relationship. To alleviate this problem, in this paper, we propose a novel mechanism to achieve the association between sensor data and domain ontology. In our approach, we first classify the sensor data by making them as SSN (Semantic Sensor Network) ontology instances, and map the corresponding instances to the concepts in the domain ontology. Secondly, a multi-strategy similarity calculation method is used to evaluate the similarity of the concept pairs between the domain ontologies at multiple levels. Finally, the set of concept pairs with a high similarity is selected by the analytic hierarchy process to construct the mapping relationship between the domain ontologies, and then the correlation between sensor data and domain ontologies are constructed. Using the method presented in this paper, we perform sensor data correlation experiments with a simulator for a real world scenario. By comparison to other methods, the experimental results confirm the effectiveness of the proposed approach.
Highlights
Various intelligent Internet of Things (IoT) based algorithms [1] and applications [2]have been developed by making use of large amount of sensor data, for example, mobile data reception in wireless sensor networks [3], and various applications in urban sustainable development [4].To optimize the utilization of data from multiple sources for decision making, meaningful sensor data should be achieved [5,6]
In order to verify that our method is effective in the practical application of ontology correlation, we introduce the experimental results of the case study of semantic inference for berth management
In order to extract the concepts and attributes corresponding to the sensor data in the sensor networks (SSN) ontology and make the database model corresponding to the SSN ontology model, we use the following XML mapping language pattern
Summary
Various intelligent Internet of Things (IoT) based algorithms [1] and applications [2]. To overcome the deficiencies of existing methods, we proposed a novel similarity evaluation method which utilizes multiple strategies to establish the relationship between domain ontologies and uses a random forest algorithm to perform the classification of instances in order to make better use of sensor data and reduce manual intervention. This method can reduce calculation efforts that are not critical in the analytic hierarchy process and improve the computation efficiency in the case of a large volume of data.
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