Knowledge-Powered Recommendation for an Improved Diet Water Footprint

TL;DR

The paper addresses reducing water use in food by using knowledge graphs to guide ingredient substitutions. It develops a Knowledge Graph-based recommender built on FoodKG, integrating Recipe1M, USDA, FOODON, and water-footprint data via a five-step pipeline (Source Identification, Information Extraction, Schema Alignment, Knowledge Graph Construction, User Interface Development). A neural predictor within the KG estimates nutrition and water footprint, achieving approximately $1\mathrm{s}$ latency on a graph of $20{,}778$ nodes and $13$ relation types. Demonstrations show substantial water-footprint reductions (e.g., from $20{,}135\mathrm{m^3}/\mathrm{ton}$ to $5{,}215\mathrm{m^3}/\mathrm{ton}$) and improved fat profiles, highlighting potential for healthier, more sustainable eating.

Abstract

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.

Figures (1)

• Figure 1: System Architecture consisting of five steps: Source Identification, Information Extraction, Schema Alignment, Knowledge Graph Construction, and User Interface Development.