Classifying Organizations for Food System Ontologies using Natural
  Language Processing
Tianyu Jiang Sonia Vinogradova Nathan Stringham E. Louise Earl Allan D. Hollander Patrick R. Huber Ellen Riloff R. Sandra Schillo Giorgio A. Ubbiali Matthew Lange
Our research explores the use of natural language processing (NLP) methods to
automatically classify entities for the purpose of knowledge graph population
and integration with food system ontologies. We have created NLP models that
can automatically classify organizations with respect to categories associated
with environmental issues as well as Standard Industrial Classification (SIC)
codes, which are used by the U.S. government to characterize business
activities. As input, the NLP models are provided with text snippets retrieved
by the Google search engine for each organization, which serves as a textual
description of the organization that is used for learning. Our experimental
results show that NLP models can achieve reasonably good performance for these
two classification tasks, and they rely on a general framework that could be
applied to many other classification problems as well. We believe that NLP
models represent a promising approach for automatically harvesting information
to populate knowledge graphs and aligning the information with existing
ontologies through shared categories and concepts.