Knowledge-Powered Recommendation for an Improved Diet Water Footprint
원문 초록
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.
함께 살펴볼 논문
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AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora
RAKG:Document-level Retrieval Augmented Knowledge Graph Construction
Type Information-Assisted Self-Supervised Knowledge Graph Denoising
RAS: Retrieval-And-Structuring for Knowledge-Intensive LLM Generation
LKD-KGC: Domain-Specific KG Construction via LLM-driven Knowledge Dependency Parsing
Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning
토론
이 논문과 연결된 포럼 글이 아직 없습니다.