Knowledge-Powered Recommendation for an Improved Diet Water Footprint
This work addresses water scarcity and health issues for consumers by providing a tool to make sustainable food choices, though it appears incremental as it applies existing knowledge graph methods to a specific domain.
The paper tackles the problem of unsustainable water usage in food consumption by proposing a knowledge graph-powered recommendation engine that suggests ingredient substitutes to improve nutritional value and reduce water footprint, offering a tool for promoting healthier eating and water conservation.
According to WWF, 1.1 billion people lack access to water, and 2.7 billion experience water scarcity at least one month a year. By 2025, two-thirds of the world's population may be facing water shortages. This highlights the urgency of managing water usage efficiently, especially in water-intensive sectors like food. This paper proposes a recommendation engine, powered by knowledge graphs, aiming to facilitate sustainable and healthy food consumption. The engine recommends ingredient substitutes in user recipes that improve nutritional value and reduce environmental impact, particularly water footprint. The system architecture includes source identification, information extraction, schema alignment, knowledge graph construction, and user interface development. The research offers a promising tool for promoting healthier eating habits and contributing to water conservation efforts.