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Dergiler / Black Sea Journal of Engineering and Science / 2026 / Cilt: 9 Sayı: 5

Development and Web-Based Visualization of a Domain-Specific Semantic Model for Coffee Culture and Production Processes

Ömer Doğan(BANDIRMA ONYEDI EYLUL UNIVERSITY, BANDIRMA VOCATIONAL SCHOOL)0000-0003-4815-8619
Alpay Doruk(BANDIRMA ONYEDİ EYLÜL ÜNİVERSİTESİ, MÜHENDİSLİK VE DOĞA BİLİMLERİ FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ)0000-0002-6190-288X
Sayfa
2393–2403
Yayın Tarihi
2026-09-15
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Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

This technical framework is a highly scalable, multilingual semantic infrastructure that is uniquely suited for integration into larger industrial traceability systems and provides a fundamental template for computer scientists, agritech developers, and agricultural stakeholders who want to exploit semantic web technologies for precision agriculture.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:The global agricultural supply chain is a complex network with different data silos, complicated cultivation parameters and highly subjective sensory evaluation metrics.
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Temel Bulgular & Veri:The quantitative estimation of the developed semantic model gives a 0.297 relation richness indicator, which confirms a highly interconnected and structurally complex knowledge graph, and not a simple taxonomic flat.
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Sonuç & Katkı:This technical framework is a highly scalable, multilingual semantic infrastructure that is uniquely suited for integration into larger industrial traceability systems and provides a fundamental template for computer scientists, agritech developers, and agricultural stakeholders who want to exploit semantic web technologies for precision agriculture.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Semantic web · Knowledge graph · Description logic

Özet

The global agricultural supply chain is a complex network with different data silos, complicated cultivation parameters and highly subjective sensory evaluation metrics. Formal knowledge representation is a must, if real semantic interoperability is to be achieved across this multidimensional domain. This work consists of the rigorous design, implementation and quantitative evaluation of the “Coffee Culture and Production Processes Ontology” and a custom client-side web-application named the “Coffee Ontology Explorer”. The semantic model, developed with standard ontology design principles and the Web Ontology Language (OWL), captures the entire coffee lifecycle from botanical taxonomy and harvesting methods to specific roasting profiles and subjective brewing evaluations. Meanwhile, the React.js based ontology explorer tackles the ongoing limitations of traditional, heavy-client ontology visualization tools by utilizing a browser-native document parsing application programming interface for high efficiency, serverless ontology parsing. The quantitative estimation of the developed semantic model gives a 0.297 relation richness indicator, which confirms a highly interconnected and structurally complex knowledge graph, and not a simple taxonomic flat. Moreover, the performance profiling of the web application shows less than one second for parsing and less than 200 milliseconds for rendering complex graphical data even for large ontological hierarchies. This technical framework is a highly scalable, multilingual semantic infrastructure that is uniquely suited for integration into larger industrial traceability systems and provides a fundamental template for computer scientists, agritech developers, and agricultural stakeholders who want to exploit semantic web technologies for precision agriculture.

Abstract

The global agricultural supply chain is a complex network with different data silos, complicated cultivation parameters and highly subjective sensory evaluation metrics. Formal knowledge representation is a must, if real semantic interoperability is to be achieved across this multidimensional domain. This work consists of the rigorous design, implementation and quantitative evaluation of the “Coffee Culture and Production Processes Ontology” and a custom client-side web-application named the “Coffee Ontology Explorer”. The semantic model, developed with standard ontology design principles and the Web Ontology Language (OWL), captures the entire coffee lifecycle from botanical taxonomy and harvesting methods to specific roasting profiles and subjective brewing evaluations. Meanwhile, the React.js based ontology explorer tackles the ongoing limitations of traditional, heavy-client ontology visualization tools by utilizing a browser-native document parsing application programming interface for high efficiency, serverless ontology parsing. The quantitative estimation of the developed semantic model gives a 0.297 relation richness indicator, which confirms a highly interconnected and structurally complex knowledge graph, and not a simple taxonomic flat. Moreover, the performance profiling of the web application shows less than one second for parsing and less than 200 milliseconds for rendering complex graphical data even for large ontological hierarchies. This technical framework is a highly scalable, multilingual semantic infrastructure that is uniquely suited for integration into larger industrial traceability systems and provides a fundamental template for computer scientists, agritech developers, and agricultural stakeholders who want to exploit semantic web technologies for precision agriculture.

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Bu Makaleye Atıf Yap

Doğan, Ö., Doruk, A. (2026). Development and Web-Based Visualization of a Domain-Specific Semantic Model for Coffee Culture and Production Processes. Black Sea Journal of Engineering and Science, Cilt: 9 Sayı: 5, 2393–2403. https://doi.org/10.34248/bsengineering.1991498

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