Semi-Automated Construction of Food Composition Knowledge Base

• URL: http://arxiv.org/abs/2301.11322v1
• Date: Tue, 24 Jan 2023 22:08:49 GMT
• Title: Semi-Automated Construction of Food Composition Knowledge Base
• Authors: Jason Youn, Fangzhou Li, Ilias Tagkopoulos
• Abstract summary: We propose a semi-automated framework for constructing a knowledge base of food composition from the scientific literature available online. Our work demonstrates how human-in-the-loop models are a step toward AI-assisted food systems that scale well to the ever-increasing big data.
• Score: 0.06445605125467573
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A food composition knowledge base, which stores the essential phyto-, micro-, and macro-nutrients of foods is useful for both research and industrial applications. Although many existing knowledge bases attempt to curate such information, they are often limited by time-consuming manual curation processes. Outside of the food science domain, natural language processing methods that utilize pre-trained language models have recently shown promising results for extracting knowledge from unstructured text. In this work, we propose a semi-automated framework for constructing a knowledge base of food composition from the scientific literature available online. To this end, we utilize a pre-trained BioBERT language model in an active learning setup that allows the optimal use of limited training data. Our work demonstrates how human-in-the-loop models are a step toward AI-assisted food systems that scale well to the ever-increasing big data.

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