Towards Semantic Sensor Data: An Ontology Approach

Area of Science:

• Computer Science
• Information Science
• Artificial Intelligence

Background:

• Optimizing intelligent applications requires effective interpretation and reuse of diverse sensor data.
• Semantic maps between heterogeneous ontologies are crucial for knowledge reuse, but current methods often overlook sensor instance data.

Purpose of the Study:

• To propose a novel mechanism for associating sensor data with domain ontologies.
• To enhance knowledge reuse in intelligent applications by improving semantic mapping between ontologies.

Main Methods:

• Classifying sensor data as Semantic Sensor Network (SSN) ontology instances and mapping them to domain ontology concepts.
• Employing a multi-strategy similarity calculation to assess concept pair similarity across domain ontologies.
• Utilizing the analytic hierarchy process to select high-similarity concept pairs for constructing ontology mappings and sensor data correlations.

Main Results:

• The proposed approach successfully establishes correlations between sensor data and domain ontologies.
• Experimental results demonstrate the effectiveness of the novel mechanism in a simulated real-world scenario.

Conclusions:

• The developed method offers an effective way to associate sensor data with domain ontologies, outperforming existing approaches.
• This work contributes to improved knowledge reuse and the optimization of intelligent sensor-driven applications.