# Applications of knowledge graphs for food science and industry

arXiv: https://arxiv.org/abs/2107.05869 · HTML: https://mlsanity.com/paper/2107.05869/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 152 references that are in the map.
- First posted on arXiv 13 Jul 2021.

## Builds on (most cited first, 27 of 27)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Scene Graph Generation by Iterative Message Passing](https://mlsanity.com/paper/1701.02426/) (2017)
- [Visual Relationship Detection with Language Priors](https://mlsanity.com/paper/1608.00187/) (2016)
- [A Review of Relational Machine Learning for Knowledge Graphs](https://mlsanity.com/paper/1503.00759/) (2015)
- [Variational Reasoning for Question Answering with Knowledge Graph](https://mlsanity.com/paper/1709.04071/) (2017)
- [RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems](https://mlsanity.com/paper/1803.03467/) (2018)
- [Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images](https://mlsanity.com/paper/1810.06553/) (2018)
- [MMKG: Multi-Modal Knowledge Graphs](https://mlsanity.com/paper/1903.05485/) (2019)
- [RecipeQA: A Challenge Dataset for Multimodal Comprehension of Cooking Recipes](https://mlsanity.com/paper/1809.00812/) (2018)
- [The More You Know: Using Knowledge Graphs for Image Classification](https://mlsanity.com/paper/1612.04844/) (2016)
- [A Survey on Food Computing](https://mlsanity.com/paper/1808.07202/) (2018)
- [Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food](https://mlsanity.com/paper/2103.03375/) (2021)
- [Unsupervised Representation Learning by Predicting Random Distances](https://mlsanity.com/paper/1912.12186/) (2019)
- [How to make a pizza: Learning a compositional layer-based GAN model](https://mlsanity.com/paper/1906.02839/) (2019)
- [5* Knowledge Graph Embeddings with Projective Transformations](https://mlsanity.com/paper/2006.04986/) (2020)
- [Structure-Aware Generation Network for Recipe Generation from Images](https://mlsanity.com/paper/2009.00944/) (2020)
- [Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph](https://mlsanity.com/paper/2101.01775/) (2021)
- [Introduction to Neural Network based Approaches for Question Answering over Knowledge Graphs](https://mlsanity.com/paper/1907.09361/) (2019)
- [Multi-task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphs](https://mlsanity.com/paper/1708.04828/) (2017)
- [An Artificial Intelligence-Based System to Assess Nutrient Intake for Hospitalised Patients](https://mlsanity.com/paper/2003.08273/) (2020)
- [Applying Personal Knowledge Graphs to Health](https://mlsanity.com/paper/2104.07587/) (2021)
- [Kissing Cuisines: Exploring Worldwide Culinary Habits on the Web](https://mlsanity.com/paper/1610.08469/) (2016)
- [An Information Extraction and Knowledge Graph Platform for Accelerating Biochemical Discoveries](https://mlsanity.com/paper/1907.08400/) (2019)
- [Semantic Modeling for Food Recommendation Explanations](https://mlsanity.com/paper/2105.01269/) (2021)

## Built on by (most cited first, 8 of 8)

- [FoodSAM: Any Food Segmentation](https://mlsanity.com/paper/2308.05938/) (2023)
- [FoodSky: A Food-oriented Large Language Model that Passes the Chef and Dietetic Examination](https://mlsanity.com/paper/2406.10261/) (2024)
- [OVFoodSeg: Elevating Open-Vocabulary Food Image Segmentation via Image-Informed Textual Representation](https://mlsanity.com/paper/2404.01409/) (2024)
- [Healthcare Knowledge Graph Construction: State-of-the-art, open issues, and opportunities](https://mlsanity.com/paper/2207.03771/) (2022)
- [An approach based on Open Research Knowledge Graph for Knowledge Acquisition from scientific papers](https://mlsanity.com/paper/2308.12981/) (2023)
- [From "What to Eat?" to Perfect Recipe: ChefMind's Chain-of-Exploration for Ambiguous User Intent in Recipe Recommendation](https://mlsanity.com/paper/2509.18226/) (2025)
- [Building FKG.in: a Knowledge Graph for Indian Food](https://mlsanity.com/paper/2409.00830/) (2024)
- [Enhancing FKG.in: automating Indian food composition analysis](https://mlsanity.com/paper/2412.05248/) (2024)

## Abstract (arXiv)

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.