Solving Sensor Ontology Metamatching Problem with Compact Flower Pollination Algorithm

• Main Authors: Feng, J. (Author), Fu, L. (Author), Lian, W. (Author), Niu, X. (Author), Wang, J.-H (Author)
• Format: Article
• Language: English
• Published: Hindawi Limited 2022
• Subjects: Benchmarking; Compact encoding; Economic and social effects; Feasible solution; Internet of things; Large-scales; Matching problems; Ontology; Ontology alignment; Ontology matching; Ontology's; Semantic relationships; Semantics; Sensor ontologies; Sensors data
• Online Access: View Fulltext in Publisher

 To implement co-operation among applications on the Internet of Things (IoT), we need to describe the meaning of diverse sensor data with the sensor ontology. However, there exists a heterogeneity issue among different sensor ontologies, which hampers their communications. Sensor ontology matching is a feasible solution to this problem, which is able to map the identical ontology entity pairs. This work investigates the sensor ontology meta-matching problem, which indirectly optimizes the sensor ontology alignment's quality by tuning the weights to aggregate different ontology matchers. Due to the largescale entity and their complex semantic relationships, swarm intelligence (SI) based techniques are emerging as a popular approach to optimize the sensor ontology alignment. Inspired by the success of the flower pollination algorithm (FPA) in the IoT domain, this work further proposes a compact FPA (CFPA), which introduces the compact encoding mechanism to improve the algorithm's efficiency, and on this basis, the compact exploration and exploitation operators are proposed, and an adaptive switching probability is presented to trade-off these two searching strategies. The experiment uses the ontology alignment evaluation initiative (OAEI)'s benchmark and the real sensor ontologies to test CFPA's performance. The statistical comparisons show that CFPA significantly outperforms other state-of-the-art sensor ontology matching techniques. © 2022 Wenwu Lian et al.