Ontology Based Ocean Knowledge Representation for Semantic Information Retrieval

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 SWbase systemto 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.

Integration of hydrological observations into a Spatial Data Infrastructure under a Sensor Web environment

Various sensors connected to the World Wide Web are used to obtain real-time hydrological observations.Thus,real-time management and utilization of such distributed in situ observations in the cyber-physical environment becomes possible.A Sensor Observation Service(SOS)chaining Web Feature Service(WFS)method is proposed to integrate geographical reference observation data collected by a hydrological Sensor Web into a virtual globe.This method hides the complexity of a series of information and service models in the Sensor Web realm to enable the integration of heterogeneous distributed hydrological data sources into a Spatial Data Infrastructure(SDI).The core components-a dynamic schema transformer and automatic information extractor-were designed and implemented.The SOS schema is matched to WFS schema that uses the schema transformer dynamically.The information extractor extracts and serves features automatically,conforming to standard SOS operations for observation retrieval and insertion.Feasibility experiments conducted on the Jinsha River tested this proposed method.Results show that the proposed approach allows the integration of SOS servers into legacy applications that have a higher degree of availability within many SDIs.However,this is accompanied with the drawback that only a limited part of the SOS functionality is available to clients.