CookingSense: A Large-Scale Culinary Knowledge Base with Multidisciplinary Assertions

Core Concepts

CookingSense is a large-scale culinary knowledge base constructed from diverse data sources, including web content, scientific papers, and recipes, to provide comprehensive and multifaceted culinary knowledge.

Abstract

This paper introduces CookingSense, a large-scale culinary knowledge base that aims to capture a broad range of culinary knowledge from various sources. The key highlights are: Data Sources: Web data: Utilized the Colossal Clean Crawled Corpus (C4) as the base corpus for general culinary and food-related information. Scientific papers: Collected a large amount of scientific literature using the Semantic Scholar Public API, focusing on culinary arts, nutrition, and food science. Recipes: Used the Recipe1M+ dataset, which contains over 1 million culinary recipes. Knowledge Extraction and Filtering: Assertions: The textual data from the sources were split or merged into chunks of one or two sentences to create "assertions" as the unit of knowledge. Filtering: Various filtering techniques were applied to remove non-generic and irrelevant assertions, including dictionary-based filtering and language model-based semantic filtering. Semantic Categorization: The assertions were categorized into six distinct types: (a) Food Common Sense, (b) Culinary Arts, (c) Healthy Diet & Nutrition, (d) Culinary Culture, (e) Food Management & Food Safety, and (f) Irrelevant or None. A classification model based on the BERT architecture was trained on a balanced dataset and applied to the full set of assertions. Evaluation: FoodBench: A novel benchmark framework was developed to assess the capabilities of culinary decision-making systems, including question answering, flavor prediction, and cultural perspective prediction. Experiments: The performance of retrieval-augmented language models was evaluated using FoodBench, demonstrating that CookingSense significantly improves the performance compared to other baseline knowledge bases. Qualitative Analysis: The analysis showed that CookingSense contains a wide array of information from diverse sources, providing rich textual representations of culinary assertions covering various aspects, including common sense, culinary arts, health and nutrition, and cultural perspectives. Overall, CookingSense is a comprehensive culinary knowledge base that can serve as a valuable resource for developing and evaluating culinary decision support systems.

Stats

CookingSense contains a total of 54,722,485 assertions. The distribution of assertions by source and type is as follows: Web: 34,314,403 assertions Paper: 35,090 assertions Recipe: 20,372,992 assertions Food Common Sense: 7,214,145 assertions Culinary Arts: 26,004,278 assertions Healthy Diet & Nutrition: 6,232,918 assertions Culinary Culture: 4,415,200 assertions Food Management & Food Safety: 10,855,944 assertions

Quotes

"Cooking is one of the most important human activities; it not only fulfills the physiological needs of humans but also facilitates a physically and emotionally healthy life." "Cooking knowledge should be defined in a multifaceted way in order to cover a broad range of topics specialized for each group, such as food common sense, culinary arts, health, nutrition, culinary culture, food management, food safety, and so on."

Key Insights Distilled From

CookingSense: A Culinary Knowledgebase with Multidisciplinary Assertions

by Donghee Choi... at arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00523.pdf

Deeper Inquiries

How can CookingSense be further expanded to include knowledge from low-resource languages and better reflect cultural nuances?

Expanding CookingSense to include knowledge from low-resource languages and better reflect cultural nuances can be achieved through several strategies: Data Collection: Low-Resource Languages: Collaborate with linguists and experts in the respective languages to source and translate culinary knowledge into English. Utilize machine translation tools to bridge the language gap. Cultural Nuances: Collect data from diverse cultural sources, including traditional cookbooks, indigenous communities, and culinary experts from various regions. Incorporate folklore, rituals, and traditions related to food preparation. Collaboration: Partner with local communities, cultural organizations, and culinary schools to gather authentic and region-specific culinary knowledge. Engage with native speakers to ensure accurate translation and interpretation of cultural nuances. Annotation and Categorization: Develop a comprehensive ontology that captures cultural concepts, rituals, and practices related to food. Annotate data with cultural metadata to enable better categorization and retrieval of culturally relevant information. Implement a crowdsourcing platform to involve individuals from diverse cultural backgrounds in annotating and validating cultural nuances in the data. Machine Learning Models: Train machine learning models on diverse datasets that include information from low-resource languages and cultural contexts. Fine-tune models to recognize and generate content that respects cultural sensitivities and nuances. Use transfer learning techniques to leverage knowledge from well-represented languages and cultures to enhance the understanding and generation of content in underrepresented languages and cultures. Evaluation and Feedback: Continuously evaluate the performance of CookingSense in capturing and representing knowledge from low-resource languages and diverse cultural contexts. Incorporate feedback loops to improve the coverage and accuracy of cultural nuances in the knowledge base.

How can the potential biases and limitations in the data sources and construction pipeline of CookingSense be addressed?

Addressing potential biases and limitations in the data sources and construction pipeline of CookingSense is crucial to ensure the quality and reliability of the knowledge base. Here are some strategies to mitigate these issues: Bias Detection: Conduct bias audits to identify and mitigate biases in the data sources, such as gender, cultural, or regional biases. Implement bias detection algorithms to flag and address biased content during the data collection and filtering process. Diverse Data Sources: Incorporate data from a wide range of sources to reduce bias and ensure a comprehensive representation of culinary knowledge. Include sources that reflect diverse cultural perspectives, dietary practices, and culinary traditions. Ethical Guidelines: Establish ethical guidelines for data collection, annotation, and curation to ensure the respectful representation of cultural nuances and sensitive information. Implement protocols for handling data privacy and confidentiality. Transparency and Accountability: Maintain transparency in the data collection and processing methods to allow for external scrutiny and validation. Document the construction pipeline to track the sources of information and the decisions made during data filtering and categorization. Diversity in Annotation: Ensure diversity in the annotation process by involving annotators from different cultural backgrounds and linguistic expertise. Incorporate mechanisms to address and rectify biases identified during the annotation phase. Regular Audits and Updates: Conduct regular audits of the data sources and the knowledge base to identify and rectify biases or inaccuracies. Update the knowledge base with new information and corrections based on feedback and emerging culinary trends.

How can the CookingSense knowledge base be integrated with large language models to develop more advanced culinary decision support systems that can handle complex reasoning and generation tasks?

Integrating the CookingSense knowledge base with large language models can enhance the capabilities of culinary decision support systems. Here's how this integration can be leveraged for complex reasoning and generation tasks: Knowledge Augmentation: Use CookingSense as a knowledge repository to augment the training data for large language models. Incorporate culinary facts, recipes, nutritional information, and cultural insights from CookingSense to enrich the model's understanding of the culinary domain. Fine-Tuning: Fine-tune large language models, such as GPT-3 or BERT, on the CookingSense data to specialize them for culinary applications. Adapt the model's parameters to prioritize culinary knowledge and improve performance on culinary tasks. Contextual Understanding: Develop specialized prompts and context windows that leverage CookingSense knowledge for culinary decision-making scenarios. Enable the model to retrieve relevant information from CookingSense to provide contextually appropriate responses. Complex Reasoning: Train the language model to perform complex reasoning tasks, such as recipe generation, ingredient substitutions, or dietary recommendations, by leveraging the structured and unstructured data in CookingSense. Enable the model to infer relationships and make informed decisions based on the knowledge base. Generation Tasks: Utilize CookingSense to guide the generation of culinary content, such as recipe variations, cooking tips, or nutritional advice. Enable the model to generate coherent and contextually relevant outputs by drawing on the diverse knowledge stored in CookingSense. Evaluation and Iteration: Continuously evaluate the performance of the integrated system on a diverse set of culinary tasks and scenarios. Collect user feedback to refine the model's responses and improve its ability to handle complex reasoning and generation tasks in the culinary domain.