Summary of Building Fkg.in: a Knowledge Graph For Indian Food, by Saransh Kumar Gupta et al.
Building FKG.in: a Knowledge Graph for Indian Food
by Saransh Kumar Gupta, Lipika Dey, Partha Pratim Das, Ramesh Jain
First submitted to arxiv on: 1 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an automated system for assimilating culinary information about Indian food using ontology design, knowledge engineering, and multilingual semantic reasoning techniques. The goal is to create a knowledge graph that captures comprehensive information on food, recipes, ingredients, cooking characteristics, and nutrition at scale. The proposed workflow uses AI, LLM, and language technology to curate recipe blog site data for building knowledge graphs. The ontology design is application-agnostic and can be used for smart analysis, recommendation systems, and integrating contextual information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a system that helps understand Indian food recipes. They’re using special computer programs (ontology design) to make it work. It’s like creating a big book of all the different types of food, ingredients, and how they’re used in cooking. They’re also trying to figure out how to get information from lots of different places on the internet (recipe blogs). The system can be used for many things, like helping people with special diets or giving suggestions for what to cook. |
Keywords
» Artificial intelligence » Knowledge graph
