CookingSense: A Culinary Knowledgebase with Multidisciplinary Assertions
This work addresses the need for structured culinary knowledge to enhance AI systems in food-related applications, though it is incremental in building on existing knowledge extraction methods.
The paper introduces CookingSense, a culinary knowledgebase built from diverse sources using filtering techniques, and FoodBench, a benchmark for evaluating culinary decision support systems, showing that CookingSense improves retrieval augmented language model performance.
This paper introduces CookingSense, a descriptive collection of knowledge assertions in the culinary domain extracted from various sources, including web data, scientific papers, and recipes, from which knowledge covering a broad range of aspects is acquired. CookingSense is constructed through a series of dictionary-based filtering and language model-based semantic filtering techniques, which results in a rich knowledgebase of multidisciplinary food-related assertions. Additionally, we present FoodBench, a novel benchmark to evaluate culinary decision support systems. From evaluations with FoodBench, we empirically prove that CookingSense improves the performance of retrieval augmented language models. We also validate the quality and variety of assertions in CookingSense through qualitative analysis.