Multi-ontology mapping generative adversarial network in internet of things for ontology alignment

Abstract

On the Semantic web, ontologies are thought to be the remedy to data heterogeneity, and correlating ontologies is a highly effective technique. Although the use of representation learning approaches to a variety of applications has showed significant promise, they have had little effect on the issue of ontology matching and classification. In order to establish alignments between two ontologies, this research presents the Multi-Ontology Mapping Generative Adversarial Network in Internet of Things (MOMGANI). For the instance of ontology mapping, we suggest using a two-system representation learning network consisting of a Generator and Discriminator. The Generator applies a probabilistic softmax classifier to the different Name, Label, Comments, Properties, Instance descriptions, concept characteristics, and the neighbourhood concepts for each of the ontology's properties. In order to support the assertions that the Generator has generated, the Discriminator network employs a novel Bidirectional Long Short-Term Memory (Bi-LSTM network) with an Ontology Attention mechanism enhanced by the concept's descriptions. As a result, both systems are in a feedback mechanism where they can learn from one another. The system will produce a set of triples that list all the associated concepts from various ontologies as its final product. Domain experts will review these triples outside of the band to ensure that only true concepts and triples are chosen for the alignment. In comparison to using the ontologies separately, the aligned ontology enables extended querying and inference across related ontologies and domains. Considering metrics like recall, precision, and F-measure, the experimental evaluation was performed utilizing the datasets for classes alignment, property alignment, and instances alignment. The proposed architecture provides a recall, precision, and F-measure of 0.92, 0.99, and 0.83 respectively which reveals that this model outperforms the traditional methods.