Semantic Web for Meteorological and Oceanographic Data

• Main Authors: Danyaro, K.U., Liew, M.S.
• Format: ["eprint_typename_conference\_item" not defined]
• Published: Institute of Electrical and Electronics Engineers Inc. 2022
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126808236&doi=10.1109%2fICCIT52419.2022.9711621&partnerID=40&md5=6f86edbcab3ecaa287ad65e8463fbf2a; http://eprints.utp.edu.my/33765/
• Institution: Universiti Teknologi Petronas

Data science research is now transforming the world of data and information technology into a new crucial paradigm. Many academic researchers have gotten interested in this matter. The goal of this study is to provide a semantic web technique for the oil and gas industry that includes an architecture for data integration. Semantic web technologies allow the process of extending the web in which information can be exchanged and shared in a meaningful way. However, with the amount of data increasing every day, there is a need for a semantic web system in all data industries. The modelling of ontologies and relational databases (RDB) into a resource description framework (RDF) is an important part of constructing the semantic web, which this study has adopted. Oil and gas data, in particular, meteorological and oceanographic (MetOcean) data, was used in this study. Applications such as Database to RDF Query (D2RQ), protégé, and web ontology language (OWL) reasoners were utilised for setup and performance in putting the findings into practice. The Berlin SPARQL Benchmark (BSBM) was used to analyze the performance of MetOceanSemWeb in order to provide a complete assessment of the findings. MetOceanSemWeb has therefore proven to be sufficient for adoption in the MetOcean sector. Furthermore, a scalability on D2RQ system was discovered due to the performance comparison. © 2022 IEEE.