Semi-Automated Construction of Food Composition Knowledge Base
This work addresses the time-consuming manual curation processes in food science for researchers and industry, though it is incremental as it applies existing NLP methods to a new domain.
The authors tackled the problem of constructing a food composition knowledge base by proposing a semi-automated framework using a pre-trained BioBERT model in an active learning setup, which enables efficient use of limited training data to extract information from scientific literature.
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.