MAD-Onto: an ontology design for mobile app development.

IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Frontiers in Artificial Intelligence Pub Date : 2025-02-03 eCollection Date: 2025-01-01 DOI:10.3389/frai.2025.1508225
Bilal Abu-Salih, Marwan Al-Tawil, Ansar Khoury, Dana A Al-Qudah, Isra'a Abu Zaid, Marwa Alabdale, Dima Azar
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引用次数: 0

Abstract

Introduction: Mobile app development has rapidly evolved into a crucial aspect of modern technology, driving innovation across various industries and transforming user experiences globally. The dynamic nature of mobile technology requires developers to navigate a complex landscape of platforms, devices, and user requirements. Effective management and sharing of knowledge are essential to address these challenges, ensuring streamlined development processes and enhanced collaboration among stakeholders.

Methods: To this end, ontologies have emerged as powerful tools for structuring and standardizing domain-specific knowledge. This paper introduces MAD-onto, a comprehensive ontology designed specifically for the mobile app development domain. The ontology is constructed by identifying key concepts, defining classes and their hierarchies, establishing class properties, and creating instances relevant to mobile app development. To ensure robustness, the ontology is evaluated using a multi-criteria evaluation metric, focusing on consistency, completeness, conciseness, expandability, and sensitiveness. Additionally, SWRL rules are applied to validate and enforce logical constraints within the ontology.

Results: Through these rigorous evaluation methods, MAD-onto demonstrates its utility in providing a structured framework for the mobile app development lifecycle, facilitating better decision-making, collaboration, and efficiency.

Discussion: The findings highlight the significance of ontology-driven approaches in addressing the complexities of mobile app development and set a foundation for future research and advancements in this field.

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来源期刊
CiteScore
6.10
自引率
2.50%
发文量
272
审稿时长
13 weeks
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