Classifying Organizations for Food System Ontologies using Natural Language Processing

TL;DR

This paper demonstrates that NLP models can effectively classify organizations based on environmental and industrial categories using web-sourced textual descriptions, aiding knowledge graph population in food systems.

Contribution

The study introduces a general NLP framework for classifying organizations into categories relevant to food system ontologies, using web-scraped descriptions for improved knowledge integration.

Findings

NLP models achieved good classification performance.

The framework is adaptable to various classification tasks.

Web-sourced descriptions are effective for entity classification.

Abstract

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.