Vol.70, No.3, 2022, pp.4707-4724, doi:10.32604/cmc.2022.020095
OPEN ACCESS
ARTICLE
Ontology Based Ocean Knowledge Representation for Semantic Information Retrieval
  • Anitha Velu*, Menakadevi Thangavelu
Department of Electronics and Communication Engineering, Adhiyamaan College of Engineering, Hosur, 635109, Tamil Nadu, India
* Corresponding Author: Anitha Velu. Email:
Received 09 May 2021; Accepted 18 July 2021; Issue published 11 October 2021
Abstract
The drastic growth of coastal observation sensors results in copious data that provide weather information. The intricacies in sensor-generated big data are heterogeneity and interpretation, driving high-end Information Retrieval (IR) systems. The Semantic Web (SW) can solve this issue by integrating data into a single platform for information exchange and knowledge retrieval. This paper focuses on exploiting the SW base system to provide interoperability through ontologies by combining the data concepts with ontology classes. This paper presents a 4-phase weather data model: data processing, ontology creation, SW processing, and query engine. The developed Oceanographic Weather Ontology helps to enhance data analysis, discovery, IR, and decision making. In addition to that, it also evaluates the developed ontology with other state-of-the-art ontologies. The proposed ontology’s quality has improved by 39.28% in terms of completeness, and structural complexity has decreased by 45.29%, 11% and 37.7% in Precision and Accuracy. Indian Meteorological Satellite INSAT-3D’s ocean data is a typical example of testing the proposed model. The experimental result shows the effectiveness of the proposed data model and its advantages in machine understanding and IR.
Keywords
Heterogeneous climatic data; information retrieval; semantic web; sensor observation services; knowledge representation; ontology
Cite This Article
Velu, A., Thangavelu, M. (2022). Ontology Based Ocean Knowledge Representation for Semantic Information Retrieval. CMC-Computers, Materials & Continua, 70(3), 4707–4724.
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