Journal
PEERJ COMPUTER SCIENCE
Volume -, Issue -, Pages -Publisher
PEERJ INC
DOI: 10.7717/peerj-cs.602
Keywords
Sensor Ontology Matching; Siamese Neural Networks; Alignment Refinement
Categories
Funding
- Natural Science Foundation of Fujian Province [2020J01875, 2019J01771]
- National Natural Science Foundation of China [61801527, 61103143]
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The paper introduces a Siamese Neural Network based Ontology Matching technique for aligning sensor ontologies, which efficiently determines high-quality sensor ontology alignments. By using representative concepts extraction method and alignment refining method, the technique enhances model performance and alignment quality.
Sensors have been growingly used in a variety of applications. The lack of semantic information of obtained sensor data will bring about the heterogeneity problem of sensor data in semantic, schema, and syntax levels. To solve the heterogeneity problem of sensor data, it is necessary to carry out the sensor ontology matching process to determine correspondences among heterogeneous sensor concepts. In this paper, we propose a Siamese Neural Network based Ontology Matching technique (SNN-OM) to align the sensor ontologies, which does not require the utilization of reference alignment to train the network model. In particular, a representative concepts extraction method is presented to enhance the model's performance and reduce the time of the training process, and an alignment refining method is proposed to enhance the alignments' quality by removing the logically conflict correspondences. The experimental results show that SNN-OM is capable of efficiently determining high-quality sensor ontology alignments.
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