[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2651":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"view_count":31,"doi":32,"paper":33,"created_at":51},2651,"Development and Web-Based Visualization of a Domain-Specific Semantic Model for Coffee Culture and Production Processes","https:\u002F\u002Fdoi.org\u002F10.34248\u002Fbsengineering.1991498","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.","全球农业供应链是一个复杂的网络，包含不同的数据孤岛、复杂的种植参数以及高度主观的感官评价指标。若要实现这一多维领域真正的语义互操作，形式化知识表示必不可少。本研究包括“咖啡文化与生产过程本体”（Coffee Culture and Production Processes Ontology）的严格设计、实现与定量评估，以及一个名为“咖啡本体浏览器”（Coffee Ontology Explorer）的定制客户端网页应用。该语义模型依据标准本体设计原则并使用Web本体语言（OWL）开发，涵盖从植物分类学和采收方法到特定烘焙曲线及主观冲泡评价的整个咖啡生命周期。与此同时，基于React.js的本体浏览器利用浏览器原生文档解析应用程序编程接口实现高效的无服务器本体解析，从而解决了传统重型客户端本体可视化工具的持续局限。对所开发语义模型的定量评估给出了0.297的关系丰富度指标，这证实了一个高度互联且结构复杂的知识图谱，而非简单的分类扁平结构。此外，该网页应用的性能分析表明，即使面对大型本体层级结构，解析时间也不到一秒，复杂图形数据的渲染时间不到200毫秒。该技术框架是一个高度可扩展的多语言语义基础设施，特别适合集成到更大的工业追溯系统中，并为希望利用语义网技术实现精准农业的计算机科学家、农业科技开发者和农业利益相关者提供了一个基础模板。",null,"Black Sea Journal of Engineering and Science","2026-09-14T00:00:00Z","论文",10,false,69,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,20,17,8,1,"咖啡全生命周期本体与浏览器端可视化工具，方法新颖、性能数据扎实，为农业语义互操作与溯源提供可复用模板，但属细分作物领域，产业影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","本体建模","咖啡产业","农业知识图谱","语义互操作",0,"10.34248\u002Fbsengineering.1991498",{"doi":32,"openalex_id":34,"authors":35,"venue":10,"cited_by_count":31,"oa_url":42,"card":43,"direction":49,"ingested_from":50},"W7213004624",[36,39],{"name":37,"orcid":38},"Ömer Doğan","https:\u002F\u002Forcid.org\u002F0000-0003-4815-8619",{"name":40,"orcid":41},"Alpay Doruk","https:\u002F\u002Forcid.org\u002F0000-0002-6190-288X","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F6208007",{"tldr":44,"method":45,"finding":46,"direction":47,"opportunity":48},"构建咖啡文化与生产过程的OWL本体，并开发浏览器端可视化工具。","用OWL本体建模咖啡全生命周期，React.js实现无服务器本体解析与可视化。","本体关系丰富度0.297，解析\u003C1秒、渲染\u003C200毫秒，可扩展至溯源系统。","数字乡村与农业信息化","可借鉴该语义本体框架，为其他农产品构建轻量级浏览器端知识图谱与溯源集成方案。","智慧农业 \u002F 农业物联网","openalex","2026-09-16T23:30:15.075138Z"]