دسته بندی:
اینترنت اشیاء - Internet of Things
سال انتشار:
2022
عنوان انگلیسی مقاله:
Multi-Ontology Mapping Generative Adversarial Network in Internet of Things for Ontology Alignment
ترجمه فارسی عنوان مقاله:
نگاشت چند هستی شناسی شبکه متخاصم مولد در اینترنت اشیا برای تراز هستی شناسی
منبع:
ScienceDirect- Elsevier- Internet of Things Available online 13 September 2022, 100616
نویسنده:
Varun M Tayur
چکیده انگلیسی:
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 ontologys 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.
Keywords: Generative adversarial network | Ontology alignment | IoT and OntoGenerator and OntoLSTM
قیمت: رایگان
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