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.
Highlights
Several marine disasters happen every year, mainly due to weather phenomena; weather prediction and analysis among ocean areas are essential
Once the datasets are presented as Resource Description Framework (RDF), many tools are available for visualizing and working with the data stored in them
It serves as a knowledge base for ocean Weather Phenomenon Prediction (WPP) and includes the hierarchy of weather conditions, attributes related to weather conditions, and their relationship
Summary
Several marine disasters happen every year, mainly due to weather phenomena; weather prediction and analysis among ocean areas are essential. Ontology Inference Layer (OIL), The Defense Advanced Research Projects Agent Markup Language (DAML)+ OIL, Web Ontology Language (OWL), Resource Description Framework (RDF), and RDF-Schema (RDF-S) are some of the computer languages used to construct ontology Among these languages, OWL is widely preferred [6]. Suppose the information published is in machine-readable format with semantic descriptions, it is easy to search and access the data dynamically by writing queries using Simple Protocol and RDF Query Language (SPARQL). These include service requests and responses at run time.
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