Classifying Organizations for Food System Ontologies using Natural Language Processing

• URL: http://arxiv.org/abs/2309.10880v1
• Date: Tue, 19 Sep 2023 19:07:48 GMT
• Title: Classifying Organizations for Food System Ontologies using Natural Language Processing
• Authors: 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
• Abstract summary: We have created NLP models that can automatically classify organizations associated with environmental issues. As input, the NLP models are provided with text snippets retrieved by the Google search engine for each organization. We believe NLP models represent a promising approach for harvesting information to populate knowledge graphs.
• Score: 9.462188694526134
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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