ReviewPatterns (New York, N.Y.)2022
Applications of knowledge graphs for food science and industry.
Review in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
18 citing papers in PubMed.
- Digital and Technology-Enabled Approaches in Dietary Assessment: Addressing Bias, Error, and Feasibility in Population- and Community-Based Research.Advances in nutrition (Bethesda, Md.) · 2026Review
- Ontology-driven dual-channel relational graph convolutional network (DCR-GCN) for lettuce leaf phenotype classification.Plant methods · 2026Article
- Multimodal AI for Real-Time Food Safety and Quality: From Sensors to Foundation Models, Edge Deployment, and Regulation.Food science & nutrition · 2026Review
- A unified knowledge graph linking foodomics to chemical-disease networks and flavor profiles.NPJ science of food · 2026Article
- Artificial Intelligence in Pharmaceutical Drug Development: Transforming Formulation and Innovation.Current drug discovery technologies · 2026Review
- Artificial Intelligence-Enabled Ingredient Substitution in Food Systems: A Review and Conceptual Framework for Sensory, Functional, Nutritional, and Cultural Optimization.Foods (Basel, Switzerland) · 2025Review
- IngredSAM: Open-World Food Ingredient Segmentation via a Single Image Prompt.Journal of imaging · 2024Article
- An Online Multimodal Food Data Exploration Platform for Specific Population Health: Development Study.JMIR formative research · 2024Article
- Integration of Diffusion Transformer and Knowledge Graph for Efficient Cucumber Disease Detection in Agriculture.Plants (Basel, Switzerland) · 2024Article
- Developing a Personalized Meal Recommendation System for Chinese Older Adults: Observational Cohort Study.JMIR formative research · 2024Article
- Disambiguity and Alignment: An Effective Multi-Modal Alignment Method for Cross-Modal Recipe Retrieval.Foods (Basel, Switzerland) · 2024Article
- Article
- Review of visual analytics methods for food safety risks.NPJ science of food · 2023Review
- PO2/TransformON, an ontology for data integration on food, feed, bioproducts and biowaste engineering.NPJ science of food · 2023Article
- Health-Aware Food Recommendation Based on Knowledge Graph and Multi-Task Learning.Foods (Basel, Switzerland) · 2023Article
- Hot Spots and Trends in the Relationship between Cancer and Obesity: A Systematic Review and Knowledge Graph Analysis.Life (Basel, Switzerland) · 2023Review
- Healthcare knowledge graph construction: A systematic review of the state-of-the-art, open issues, and opportunities.Journal of big data · 2023Article
- Semantics of Dairy Fermented Foods: A Microbiologist's Perspective.Foods (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The deployment of various networks (e.g., Internet of Things [IoT] and mobile networks), databases (e.g., nutrition tables and food compositional databases), and social media (e.g., Instagram and Twitter) generates huge amounts of food data, which present researchers with an unprecedented opportunity to study various problems and applications in food science and industry via data-driven computational methods. However, these multi-source heterogeneous food data appear as information silos, leading to difficulty in fully exploiting these food data. The knowledge graph provides a unified and standardized conceptual terminology in a structured form, and thus can effectively organize these food data to benefit various applications. In this review, we provide a brief introduction to knowledge graphs and the evolution of food knowledge organization mainly from food ontology to food knowledge graphs. We then summarize seven representative applications of food knowledge graphs, such as new recipe development, diet-disease correlation discovery, and personalized dietary recommendation. We also discuss future directions in this field, such as multimodal food knowledge graph construction and food knowledge graphs for human health.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.