用知识图谱与大模型自动分析印度菜营养成分，解决数据缺失难题。
Enhancing FKG.in: automating Indian food composition analysis
- 基于FKG.in知识图谱和大模型，构建自动化营养分析流程。
- 整合三大数据源，实现多维度食物成分信息补全与验证。
- 适合营养研究、健康推荐系统开发者使用，方法可复用于其他领域。
本文提出一种新方法，利用印度食物知识图谱FKG.in与大语言模型（LLMs）自动计算印度菜谱的营养成分数据。核心目标是建立一个自动化食物成分分析工作流，包含营养数据聚合、成分分析及大模型增强的信息解析功能，以补充并迭代完善来自可信知识库的食物成分数据。文中强调了数字化呈现印度食物时面临的挑战，回顾了三大关键数据源：印度食物成分表、印度营养数据库和Nutritionix API。同时简要说明用户如何通过该流程获取基于饮食的健康建议及大量菜谱的详细成分信息。我们还探讨了在结构、多语言性和不确定性等方面分析印度菜谱所遇复杂挑战，并展示了基于大模型的解决方案进展。本工作提出的AI驱动知识整理与信息解析方法具备应用无关性、可泛化性和可复制性，适用于任何领域。
原文摘要 · Abstract (English)
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.
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