Ontology development (OWL&amp;amp;UML) methodology of web- based Decision Support System for water management

Salah, Hussein Ali

doi:10.1109/ecai.2014.7090217

Cited by 6 publications

(5 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Garrido and Requena (2011) design an ontology for Environmental Impact Assessment (EIA) to be used directly as a reference for EIA methodology developers, as well as for managing knowledge in an expert system for EIA. Salah (2014) proposes an approach for facilitating decision-making in web-based DSSs for water management. Katsiri and Makropoulos (2016) develop DiHydro, a novel ontology that can be used to regulate the gray water reuse process, detect, and react to any failures or unexpected events, evaluate and enhance the efficiency of water reuse, and anticipate the best time for maintenance in a decentralized water management enabled sustainable smart home.…”

Section: Water Resources Management Domain Summarymentioning

confidence: 99%

“…Ontology-Based Intelligence System: Water resource management requires an intelligent system that optimizes data utilization, knowledge acquisition, and simulation procedures. Numerous studies of ontology-based intelligence system have been conducted on hydrological monitoring (Wang et al, 2017), including flow (Stephen and Hahmann, 2017), drought (Kaewboonma et al, 2014), flood (Wang et al, 2018) and mitigation (Mughal et al, 2021b), water (Howell et al, 2018) and water resources management at river (Oliva-Felipe et al, 2017;Mughal and Shaikh, 2017) and watershed scale (Salah, 2014;Oprea, 2018;Mughal et al, 2021b;Yi and Zuo, 2021) applications. In 13.5% of selected publications, systems are created with the help of machine-readable and understandable ontologies.…”

Section: Summary and Findingsmentioning

confidence: 99%

“…Ontology-Aided Simulation/Modeling/Assessment: Ontology allows simulation (Gil et al, 2016), assessment, and modeling if the mathematical formulations of important processes in any domain are known based on the representation of structured knowledge. There are some examples in this focus area for water modeling in agriculture (Kwon et al, 2010;2014), environment (Garrido and Requena, 2011), hydrology (Shu et al, 2012), and natural disaster (Li et al, 2021) research fields. 10.3% of reviewed publications performed ontology-based simulation, modeling activities, and assessment.…”

Section: Summary and Findingsmentioning

confidence: 99%

See 2 more Smart Citations

A Comprehensive Review of Ontologies in the Hydrology Towards Guiding Next Generation Artificial Intelligence Applications

Baydaroğlu¹,

Yeșilköy²,

Sermet³

et al. 2022

Preprint

View full text Add to dashboard Cite

Open science presents a new approach for knowledge discovery, dissemination, and integrity. The idea behind open science is to reinforce research activities and create open knowledge networks by exploring, organizing, and sharing scientific data, as well as making research results transparent, open and integrated. Big data derived from remote sensing, ground-based measurements, models and simulations, social media and crowdsourcing, and wide variety of structured and unstructured sources requires significant efforts for data and knowledge management. Innovations and developments in information technology for the last couple of decades have made data and knowledge management possible for insurmountable amount of data collected and generated over the last decades. This enabled open knowledge networks to be built that lead to new ideas in scientific research and business world. To design and develop open knowledge networks, ontologies are essential since they form the backbone of conceptualization of a given knowledge domain. In this article, a systematic literature review is conducted to examine research involving ontologies related to hydrological processes and water resources management. The hydrologic cycle (water cycle) is a multi-component and multi-process system that is shaped by various dynamic factors. Because all components of the hydrologic cycle interact with one another and water distribution on Earth is not uniform in time and space, modeling the hydrologic cycle and management of hydrologic cycle components are complex and difficult endeavors. Ontologies in the hydrology domain support the comprehension, monitoring, and representation of the hydrologic cycle’s complex structure, as well as the predictions of its processes. They contribute to the development of necessary ontology-based information and decision support systems, the comprehension of environmental and atmospheric phenomena, the development of climate and water resiliency concepts, the creation of educational tools with artificial intelligence, and the strengthening of related cyberinfrastructures. This review provides an explanation of key issues and challenges in ontology development based on hydrologic processes to guide development of next generation artificial intelligence applications. The authors discuss future research prospects in combination with the artificial intelligence and hydroscience.

show abstract

Section: Water Resources Management Domain Summarymentioning

confidence: 99%

Section: Summary and Findingsmentioning

confidence: 99%

Section: Summary and Findingsmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Review of Ontologies in the Hydrology Towards Guiding Next Generation Artificial Intelligence Applications

Baydaroğlu¹,

Yeșilköy²,

Sermet³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The importance of the role of ontology in developing e-learning platforms is increasingly becoming a reason of [19] to build OWL Moodle that can make the data exchanged therein can be processed by machines. Other research conducted by [20] proposes ontology development methodologies to facilitate the decision making process about water management systems used in a web-based Decision Support Systems. Manually changing data and information on repository publications are addressed in [21], [22] through the use of structured knowledge that is based on ontology design with dynamic domains.…”

Section: Related Workmentioning

confidence: 99%

Transformation of SysML Requirement Diagram into OWL Ontologies

Wardhana¹,

Ashari²,

Kartika³

2020

IJACSA

View full text Add to dashboard Cite

The Requirement Diagrams are used by the System Modeling Language (SysML) to depict and model non-functional requirements, such as response time, size, or system functionality, which cannot be accommodated in the Unified Modeling Language (UML). Nevertheless, SysML still lacks the capability to represent the semantic contexts within the design. Web Ontology Language (OWL) can be used to capture the semantic context of system design; hence, the transformation of SysML diagrams into OWL is needed. The current method of SysML Diagrams transformation into OWL is still done manually so that it is very vulnerable to errors, and the translation process requires more time and effort for system engineers. This research proposes a model that can automatically transform a SysML Requirement Diagram into an OWL file so that system designs can be easily understood by both humans and machines. It also allows users to extract knowledge contained in the previous diagrams. The transformation process makes use of a transformation rule and an algorithm that can be used to change a SysML Requirement Diagram into an OWL ontology file. XML Metadata Interchange (XMI) serialization is used as the bridge to perform the transformation. The produced ontology can be viewed in Protégé. The class and subclass hierarchy, as well as the object properties and data properties, are clearly shown. In the experiment, it is also shown that the model can conduct the transformation correctly.

show abstract

“…Among these works, we cite investigating knowledge and learning [5], medical emergency services [6], [7] ecological engineering [8], intercropping [9]. We can cite also, land use planning [10], Urban planning [11], [12], web-based intelligent SDSS [13], water management [14]. Recently, a lot of works have been interested by solving one of important and complex spatial decision problems, which is site selection.…”

mentioning

confidence: 99%

An ontology-based spatial group decision support system for site selection application

Djelloul

Hamdadou²

2023

IJECE

View full text Add to dashboard Cite

<span lang="EN-US">This paper presents a new ontology-based multicriteria spatial group decision support system (GDSS) dedicated to site selection problems. Site selection is one of the most complex problems in the construction of a new building. It presents a crucial problem in terms of selecting the appropriate site among a group of decision makers with multiple alternatives (sites); in addition, the site must satisfy several criteria. To deal with this, the present paper introduces an ontology based multicriteria analysis method to solve semantic heterogeneity in vocabulary used by participants in spatial group decision support systems. The advantages of using ontology in GDSS are many: i) it enables the integration of heterogeneous sources of data available on the web and ii) it enables to facilitate meaning and sharing of data used in GDSS by participants. In order to facilitate cooperation and collaboration between participants in GDSS, our work aims to apply ontology at the model's structuration phase. The proposed system has been successfully implemented and exploited for a personalized environment.</span>

show abstract

Ontology development (OWL&UML) methodology of web- based Decision Support System for water management

Cited by 6 publications

References 13 publications

A Comprehensive Review of Ontologies in the Hydrology Towards Guiding Next Generation Artificial Intelligence Applications

A Comprehensive Review of Ontologies in the Hydrology Towards Guiding Next Generation Artificial Intelligence Applications

Transformation of SysML Requirement Diagram into OWL Ontologies

An ontology-based spatial group decision support system for site selection application

Contact Info

Product

Resources

About