Browse > Article
http://dx.doi.org/10.7842/kigas.2021.25.3.9

Worker Symptom-based Chemical Substance Estimation System Design Using Knowledge Base  

Ju, Yongtaek (Dept. of Disaster and Safety, Myongji University)
Lee, Donghoon (Dept. of Disaster and Safety, Myongji University)
Shin, Eunji (Dept. of Disaster and Safety, Myongji University)
Yoo, Sangwoo (Dept. of Disaster and Safety, Myongji University)
Shin, Dongil (Dept. of Disaster and Safety, Myongji University)
Publication Information
Journal of the Korean Institute of Gas / v.25, no.3, 2021 , pp. 9-15 More about this Journal
Abstract
In this paper, a study on the construction of a knowledge base based on natural language processing and the design of a chemical substance estimation system for the development of a knowledge service for a real-time sensor information fusion detection system and symptoms of contact with chemical substances in industrial sites. The information on 499 chemical substances contact symptoms from the Wireless Information System for Emergency Responders(WISER) program provided by the National Institutes of Health(NIH) in the United States was used as a reference. AllegroGraph 7.0.1 was used, input triples are Cas No., Synonyms, Symptom, SMILES, InChl, and Formula. As a result of establishing the knowledge base, it was confirmed that 39 symptoms based on ammonia (CAS No: 7664-41-7) were the same as those of the WISER program. Through this, a method of establishing was proposed knowledge base for the symptom extraction process of the chemical substance estimation system.
Keywords
knowledge base; symptoms; chemical substance estimation; WISER; AllegroGraph;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Hong, G.W., Myaeng, S.H., "Generation of open relation embeddings from natural language sentences using BERT and knowledge base relation embeddings", Journal of The Korean Institute of Information Scientists and Engineers, 533-535, ICC JEJU, Korea, (2019)
2 Kingma, D. and J. Ba, Adam: A method for stochastic optimization, Proceedings of the 3rd International Conference for Learning Representations, (2015)
3 KOSHA(Korea Occupational Safety & Health Agency), http://www.kosha.or.kr/
4 NIH (National Institutes of Health), https://wiser.nlm.nih.gov/
5 Kim, J.R., Ro, K.H., "Construction of Knowledge Base Based on Graph Database for College Student Career Advice Using Public Data", Journal of The Institute of Electronics and Information Engineers, 56(10), 41-48, (2019)   DOI
6 Kim, J.H., Lee, M.J., "Knowledge Extraction Methodology and Framework from Wikipedia Articles for Construction of Knowledge-Base", Journal of Intelligence and Information Systems, 25(1), 43-61, (2019)   DOI
7 Jeong-Min Cha, Seong-Ho Hyun, "A Study on the Analysis of Domestic Hazardous Material Accidents in Recent 10 Years", Korean Journal of Hazardous Materials , 7(1), 54-64, (2019)   DOI
8 Jung, S.W., Choi, M.S., Kim, H.S., "Construction of Korean Knowledge Base Based on Machine Learning from Wikipedia", Journal of KIISE, 42(8), 1065-1070, (2015)   DOI
9 B. Saha and K. Goebel, Battery data set,"NASA AMES Prognostics Data Repository, (2007)
10 A. Jain, K. Nandakumar and A. Ross, "Score normalization in multimodal biometric systems", Pattern Recognit., 38(12), 2270-2285, (2005)   DOI