FKG.in: Knowledge Graph for Indian food
A knowledge graph for Indian food offers an ontological, structured, and semantic framework to capture the immense diversity, complexity, and contextual richness of India’s culinary systems. Unlike flat databases or recipe aggregators, a knowledge graph connects entities — ingredients, dishes, techniques, tools, health effects, cultural meanings — through meaningful relationships. This interconnected representation is essential for modeling food not just as data, but as lived knowledge shaped by geography, history, agriculture, medicine, and culture. Given India’s vast regional diversity and pluralistic food traditions, a knowledge graph allows us to make this complexity computationally accessible, enabling nuanced queries. Such a graph also becomes a foundational tool for building intelligent applications in personalized nutrition, misinformation detection, sustainable eating, adaptive recipe systems, and culinary education. Most importantly, by incorporating underrepresented culinary knowledge — especially from rural and Indigenous communities — it can help democratize food technology and preserve traditional knowledge at risk of erasure. In an era of generative AI and shifting food systems, a knowledge graph is not just a technical artifact but a cultural infrastructure for collective food intelligence.