MEMOn: Modular Environmental Monitoring Ontology to link heterogeneous Earth observed data. (February 2020)

• Record Type: Journal Article
• Authors: Masmoudi, Maroua; Karray, Mohamed Hedi; Ben Abdallah Ben Lamine, Sana; Zghal, Hajer Baazaoui; Archimede, Bernard
• Is Part Of: Environmental modelling & software. Volume 124(2020)
• Publication Date: 2020-02
• Subjects: Modular ontology -- Environmental monitoring -- Earth observation semantic interoperability data integration -- Knowledge graph; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2019.104581
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Earth observation (EO) systems play a significant role in environmental monitoring and the prediction of natural disasters. These systems generate a massive amount of heterogeneous data stored in different formats. The exploitation of this data is still limited while, in most cases, data are not linked, and sources are not interoperable. Hence, data cannot be exploited as an interoperable global knowledge graph to have more in-depth analyzes of environmental phenomena. Ontology, as a knowledge representation formalism, is a promising solution for the semantic interoperability between this data. In this work, we present a modular ontology for environmental monitoring developed based on an original agile methodology. The so-called MEMOn (Modular Environmental Monitoring Ontology) aims to support semantic interoperability, data integration, and linking of heterogeneous data collected through a variety of observation techniques and systems. We also present real use case studies to show the usefulness of the proposed ontology. Highlights: Modular ontology for environmental monitoring. Agile methodology to manage the lifecycle development of ontology. Semantic interoperability between heterogenous data. Linking and integration of environmental observed data.