Authors

* External authors

Venue

Date

Share

CookingSense: A Culinary Knowledgebase with Multidisciplinary Assertions

Donghee Choi*

Mogan Gim*

Donghyeon Park*

Mujeen Sung

Hyunjae Kim

Jaewoo Kang*

Jihun Choi

* External authors

LREC-Coling-2024

2024

Abstract

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.

Related Publications

Literature-based Hypothesis Generation: Predicting the evolution of scientific literature to support scientists

AI4X, 2025
Tarek R Besold, Uchenna Akujuobi, Samy Badreddine, Jihun Choi, Hatem ElShazly, Frederick Gifford, Chrysa Iliopoulou, Kana Maruyama, Kae Nagano, Pablo Sanchez Martin, Thiviyan Thanapalasingam, Alessandra Toniato, Christoph Wehner

Science is advancing at an increasingly quick pace, as evidenced, for instance, by the exponential growth in the number of published research articles per year [1]. On the one hand, this poses anincreasingly pressing challenge: Effectively navigating this ever-growing body o…

Link prediction for hypothesis generation: an active curriculum learning infused temporal graph-based approach

AIR, 2024
Uchenna Akujuobi, Priyadarshini Kumari, Jihun Choi, Samy Badreddine, Kana Maruyama, Sucheendra K Palaniappan*, Tarek R Besold

Over the last few years Literature-based Discovery (LBD) has regained popularity as a means to enhance the scientific research process. The resurgent interest has spurred the development of supervised and semi-supervised machine learning models aimed at making previously imp…

Analysis of Multi-Source Language Training in Cross-Lingual Transfer

ACL, 2024
Seong Hoon Lim*, Taejun Yun*, Jinhyeon Kim*, Jihun Choi, Taeuk Kim

The successful adaptation of multilingual language models (LMs) to a specific language-task pair critically depends on the availability of data tailored for that condition. While cross-lingual transfer (XLT) methods have contributed to addressing this data scarcity problem, …

  • HOME
  • Publications
  • CookingSense: A Culinary Knowledgebase with Multidisciplinary Assertions

JOIN US

Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.