Ocean knowledge representation through integration of big data employing semantic web technologies

Implementation of ocean observation sensors are booming in recent years for encouraging research among coastal areas all over the world. This results in a copious amount of big data which makes it difficult for traditional data processing applications to manage them. The complexity in ocean observing community is heterogeneity and interpretation, which directs to a high-end information retrieval system. World Wide Web Consortium (W3C) spreads the usage of Semantic Web (SW) that provide easier way to search, reuse, combine and share information by integrating the data into a single platform. The use of semantic web in big data management helps to increase end-users ability for self-management of data from various sources, to handle the concepts and relationships of a domain and to manage the terminologies while connecting data from a varied data sources. This paper focuses on integrating big data with semantic web technology by developing a knowledge base system through ontology to solve the problem of heterogeneity in ocean observing communities. Ontology refers to a set of machine-readable controlled vocabularies which interprets big data by combining the data concepts with ontology classes. The proposed data model upgrades the information system in terms of improvising data analysis, discovery, retrieval and decision making. In addition to that, this paper also evaluates the quality of proposed ontology and found to be 39.28% improved in completeness, 45.29% reduced in structural complexity, 11% and 37.7% decreased in conciseness and correctness, respectively.