An Interactive Knowledge and Learning Environment in Smart Foodsheds
An Interactive Knowledge and Learning Environment in Smart Foodsheds
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DOI：
10.1109/mcg.2023.3263960
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发表时间：
2023-04
影响因子：
1.8
通讯作者：
Yamei Tu;Xiaoqi Wang;Rui Qiu;Han-Wei Shen;Michelle Miller;Jinmeng Rao;Song Gao;P. Huber
中科院分区：
文献类型：
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作者：
Yamei Tu;Xiaoqi Wang;Rui Qiu;Han-Wei Shen;Michelle Miller;Jinmeng Rao;Song Gao;P. Huber
食品互联网（IOF）是智能食品的新兴领域，涉及有关环境，农业，食品，饮食和健康的知识图（kg）的创建。但是，KG的异质性和大小提出了下游任务的挑战，例如信息检索和互动探索。为了应对这些挑战，我们提出了一个交互式知识和学习环境（IKLE），该环境（IKLE）整合了三种编程和建模语言，以支持分析管道中的多个下游任务。为了使Ikle易于使用，我们开发了算法来自动化每种语言的生成。此外，我们与域专家合作设计和开发了数据流可视化系统，该系统将自动语言世代嵌入组件中，并允许用户通过拖动和连接感兴趣的组件来构建其分析管道。我们通过三个现实世界中的智能食品研究中的三个现实案例研究证明了Ikle的有效性。
The Internet of Food (IoF) is an emerging field in smart foodsheds, involving the creation of a knowledge graph (KG) about the environment, agriculture, food, diet, and health. However, the heterogeneity and size of the KG present challenges for downstream tasks, such as information retrieval and interactive exploration. To address those challenges, we propose an interactive knowledge and learning environment (IKLE) that integrates three programming and modeling languages to support multiple downstream tasks in the analysis pipeline. To make IKLE easier to use, we have developed algorithms to automate the generation of each language. In addition, we collaborated with domain experts to design and develop a dataflow visualization system, which embeds the automatic language generations into components and allows users to build their analysis pipeline by dragging and connecting components of interest. We have demonstrated the effectiveness of IKLE through three real-world case studies in smart foodsheds.