  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.
