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Food4healthKG: Knowledge graphs for food recommendations based on gut microbiota and mental health
Chengcheng Fu1, Zhisheng Huang2, Frank van Harmelen3
1National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China; School of Computer Science, Central China Normal University, Wuhan, China; Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; National Language Resources Monitor Research Center for Network Media, Central China Normal University, Wuhan, China.
Abstract:
Food is increasingly acknowledged as a powerful means to promote and maintain mental health. The introduction of the gut-brain axis has been instrumental in understanding the impact of food on mental health. It is widely reported that food can significantly influence gut microbiota metabolism, thereby playing a pivotal role in maintaining mental health. However, the vast amount of heterogeneous data published in recent research lacks systematic integration and application development. To remedy this, we construct a comprehensive knowledge graph, named Food4healthKG, focusing on food, gut microbiota, and mental diseases. The constructed workflow includes the integration of numerous heterogeneous data, entity linking to a normalized format, and the well-designed representation of the acquired knowledge. To illustrate the availability of Food4healthKG, we design two case studies: the knowledge query and the food recommendation based on Food4healthKG. Furthermore, we propose two evaluation methods to validate the quality of the results obtained from Food4healthKG. The results demonstrate the system's effectiveness in practical applications, particularly in providing convincing food recommendations based on gut microbiota and mental health. Food4healthKG is accessible at https://github.com/ccszbd/Food4healthKG.
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