Eco-informatics modelling via semantic inference

Abstract

a b s t r a c tThere is a demand for new and evolved research practices resulting from the so called ''data deluge'' emerging from high volume digital collection methods. As the volume of raw data increases traditional data processing methodologies, especially those involving manual manipulation are becoming increasingly difficult to manage. This paper presents the ''Semantic Reef'' architecture that offers an alternative approach to the development, application and execution of observational hypotheses involving studies of coral reef ecosystems. The Semantic Reef Knowledge Representation system is an eco-informatics application designed to assist in the integration of remotely sensed data streams and historic data sets supporting flexible hypothesis design and knowledge extraction. The system is an ontology-based architecture built to allow researchers to combine disjoint data sets into a single Knowledge Base for modelling the impact of climate change on coral reef ecosystems. The Knowledge Base consists of a hierarchy of ontologies developed to maximise usability and reusability by separating data instances from the concept descriptions. The model can be effectively reused to extract or disclose phenomena of any coral reef. This paper both demonstrates and describes a performance analysis of the Semantic Reef knowledge system.

Agent-based modelling of socio-ecological systems: Models, projects and ontologies

“…Ontologies are already in use in many areas of work, including ecosystems research. Up to now, their main use in this area has been for data integration (Poelen et al, 2014;Coetzer et al, 2017), including semi-automated processing of remote sensing data (Myers and Atkinson, 2013), rather than in simulation modelling. Usually an ABM (or any other software model of an ecosystem or SES) is described in natural language, sometimes accompanied by tables and diagrams, and possibly structured according to some protocol such as ODD (Grimm et al, 2006(Grimm et al, , 2010.…”

Semantic IoT: Intelligent Water Management for Efficient Urban Outdoor Water Conservation

“…Semantic technology data models aim to capture the meaning of data to represent real world situations for data integration and manipulation [19,20]. Formal logical paradigms are applied to automate classifications of concepts and the inference of new information.…”

“…Formal logical paradigms are applied to automate classifications of concepts and the inference of new information. The computer can make intelligent decisions based on conclusions derived through predicate and propositional logic systems embedded in explicit ontological definitions [19,20]. Each Quadrant contains a Plant of a PlantSpecies, which determines WateringRequirements.…”

STSM: An Infrastructure for Unifying Steel Knowledge and Discovering New Knowledge

“…In recent years, relatively mature domain ontologies have been established in environment [8], biology and medicine [9][10][11][12][13][14][15][16][17][18][19][20][21][22], chemistry [23][24][25][26], art [27] and other domains [28] with the rapid development of semantic web technology, and they have been applied to their respective domain. However, materials domain may have few relatively mature ontologies [29][30][31].…”