Exploring Knowledge Domain Bias on a Prediction Task for Food and Nutrition Data

Human understanding and knowledge about food and nutrition is constantly evolving, and has significantly improved recently, one of the main contributor to this is data. The possibilities of gaining knowledge from food and nutrition-related data are yet to be explored. One of the most important information about food is nutrient content, which is very relevant for patients suffering from various diseases, professional athletes, and slowly part of everyday life of many for health or fitness goals. In this paper, we explore the effect of domain bias in a predictive study in the food and nutrition domain. Having a machine learning pipeline for predicting nutrient values with learned vector representations from short text description of recipes, we introduce domain knowledge before the prediction algorithms are applied. On a large corpus of recipe data containing short description and nutrient values we introduce word and paragraph embeddings, learn concept representations for the textual descriptions, introduce domain knowledge for clustering the data, and apply machine learning algorithms for predicting the nutrient content of the recipes. We explore the impact of the domain knowledge by introducing two different criteria of clustering the dataset - using graph embedding of the FoodEx2 codes, and using the traffic light labelling system from the Food Standards Agency; at the end we compare the two different criteria. The results from this study imply that inferring domain knowledge beforehand is crucial for the task of predicting nutrient content.