abstract = "Sensor ontology is the kernel technique of the
Intelligent Sensor System, which provides a structured
framework to organize and interpret the knowledge of
the Internet of Things (IoT). However, the ontology
heterogeneity issue hampers the communication of sensor
ontologies. Sensor Ontology Matching (SOM) can find
semantically identical entities between two ontologies,
which is an effective method to address this issue.
However, due to their complicated semantic
relationships, it is a challenge to construct an
effective Similarity Feature (SF) to distinguish the
heterogeneous sensor entities. Although Evolutionary
Algorithms (EAs) based matching techniques have shown
their effectiveness in the ontology matching field,
they suffer from drawbacks such as high computational
complexity and expert-dependent solution evaluation. To
overcome these drawbacks, this paper proposes a novel
Light Genetic Programming (L-GP) to automatically
construct SF for SOM. First, a simplified evolutionary
mechanism is designed to improve the efficiency of the
SOM process. Second, a novel fitness function based on
the approximate evaluation metric is introduced to
automatically guide the search direction of L-GP.
Lastly, a two-stage tournament selection operator is
presented to balance the quality and complexity of the
solutions, improving the accuracy of the SOM results.
The experiment uses ten pairs of real-world SOM tasks
to test the performance of L-GP, and the experimental
results show that L-GP significantly outperforms
state-of-the-art matching techniques.",
notes = "Also known as \cite{10445711}
School of Computer Science and Mathematics Fujian
University of Technology Fuzhou, China",