CookingSense: A Culinary Knowledgebase with Multidisciplinary Assertions
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866910562446737408 |
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| author | Choi, Donghee Gim, Mogan Park, Donghyeon Sung, Mujeen Kim, Hyunjae Kang, Jaewoo Choi, Jihun |
| author_facet | Choi, Donghee Gim, Mogan Park, Donghyeon Sung, Mujeen Kim, Hyunjae Kang, Jaewoo Choi, Jihun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_00523 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CookingSense: A Culinary Knowledgebase with Multidisciplinary Assertions Choi, Donghee Gim, Mogan Park, Donghyeon Sung, Mujeen Kim, Hyunjae Kang, Jaewoo Choi, Jihun Artificial Intelligence Computation and Language 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. |
| title | CookingSense: A Culinary Knowledgebase with Multidisciplinary Assertions |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2405.00523 |