An efficient model for extracting an optimal alignment with multiple cardinalities in ontology alignment

Touati, Chahira; Lebbah, Yahia; Benaissa, Moussa

doi:10.1504/ijmso.2016.10001279

Cited by 1 publication

(1 citation statement)

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“…Fernandez et al [7] propose a fuzzy theory-based method of aggregating the ontology matchers. Later on, the global and local ontology alignment extracting technique [8] is presented to find the alignment from diverse alignments obtained by different ontology matchers. eir proposal is able to take each correspondence's preference into consideration, which improves the alignment's quality.…”

Section: Introductionmentioning

confidence: 99%

Solving Sensor Ontology Metamatching Problem with Compact Flower Pollination Algorithm

Lian

Niu

et al. 2022

Wireless Communications and Mobile Computing

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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.

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Section: Introductionmentioning

confidence: 99%