New article in AAAI-23 AIAFS

A new publication from Jason Youn, Fangzhou Li, and Dr. Ilias Tagkopoulos about Semi-automated construction of food composition knowledge base is accepted as a lightning talk in the 37th AAAI Conference on Artificial Intelligence, AI for Agriculture and Food Systems Workshop (AAAI-23 AIAFS).

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.

Reference: Youn, Jason, Fangzhou Li, and Ilias Tagkopoulos. “Semi-automated construction of food composition knowledge base.” The 37th AAAI Conference on Artificial Intelligence, AI for Agriculture and Food Systems Workshop (AIAFS AAAI-23), Washington, D.C., 2023 (link), (GitHub)