.png){kind=icon}
.png){kind=icon}
Return
Matching Sensor Ontologies With Multi-Context Similarity Measure and Parallel Compact Differential Evolution Algorithm
.png){kind=icon}
PRE
AI
DOI：10.1109/JSEN.2021.3115471.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Sensor ontology is able to resolve the sensor data heterogeneity issue among the Cybertwin-driven 6G based Internet of Everything (IoE) systems. However, due to human subjectivity, the sensor ontologies also suffer from the heterogeneity problem. To address this problem, it is necessary to execute the sensor Ontology Matching (OM) process, i.e., finding the identical entity pairs between two ontologies. To this end, we first propose a Multi-Context based ESM (MC-ESM) to measure the similarity of two entities by taking into consideration their semantic contexts. After that, a parallel compact Differential Evolution with Adaptive Step Length (pcDE-ASL) is proposed to find all entity mappings, which uses the probability presentation on the population to save the memory consumption, and the parallel processing mechanism and ASL to help the algorithm efficiently converge on the global optima. The experimental results show that pcDE-ASL is both effective and efficient.
Keywords:
Sensors
Ontologies
Semantics
Statistics
Sociology
Parallel processing
Internet of Things
Sensor ontology
parallel compact differential evolution algorithm
multi-context based similarity measure
Journal
IF:
4.5
Papers:
2.1W
Citations:
7.3W