Enhancing FKG.in: automating Indian food composition analysis

TL;DR

This paper introduces an automated workflow leveraging knowledge graphs and large language models to analyze Indian food recipes and composition data, addressing digital access and representation challenges.

Contribution

It presents a novel, generalizable approach combining knowledge graphs and LLMs for Indian food composition analysis, enhancing existing databases and workflows.

Findings

Workflow effectively aggregates nutrition data

LLM-augmented methods improve information resolution

Addresses multilingual and structural complexities in Indian recipes

Abstract

This paper presents a novel approach to compute food composition data for Indian recipes using a knowledge graph for Indian food (FKG[.]in) and LLMs. The primary focus is to provide a broad overview of an automated food composition analysis workflow and describe its core functionalities: nutrition data aggregation, food composition analysis, and LLM-augmented information resolution. This workflow aims to complement FKG[.]in and iteratively supplement food composition data from verified knowledge bases. Additionally, this paper highlights the challenges of representing Indian food and accessing food composition data digitally. It also reviews three key sources of food composition data: the Indian Food Composition Tables, the Indian Nutrient Databank, and the Nutritionix API. Furthermore, it briefly outlines how users can interact with the workflow to obtain diet-based health recommendations and detailed food composition information for numerous recipes. We then explore the complex challenges of analyzing Indian recipe information across dimensions such as structure, multilingualism, and uncertainty as well as present our ongoing work on LLM-based solutions to address these issues. The methods proposed in this workshop paper for AI-driven knowledge curation and information resolution are application-agnostic, generalizable, and replicable for any domain.