Knowledge graph embedding and reasoning for real-time analytics support of chemical diagnosis from exposure symptoms

[Display omitted] Chemical exposure accidents pose a risk of serious injury and property damage if the diagnosis or response is not properly performed after the initial discovery. Due to lack of research on the dynamically changing environment and detection of chemical substances considering symptom...

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Bibliographic Details
Published in:Process safety and environmental protection 2022-01, Vol.157, p.92-105
Main Authors: Shin, Eunji, Yoo, Sangwoo, Ju, Yongtaek, Shin, Dongil
Format: Article
Language:English
Subjects:
Accidents
Changing environments
Chemical Safety
Data Analytics
Diagnosis
Embedding
Emergency preparedness
Emergency response
Exposure
Exposure Symptom Knowledge
Hazardous materials
Health risks
Knowledge Graph
Knowledge Graph Embedding
Knowledge Graph Reasoning
Knowledge representation
Mitigation
Property damage
Real time
Reasoning
Time response
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Summary:[Display omitted] Chemical exposure accidents pose a risk of serious injury and property damage if the diagnosis or response is not properly performed after the initial discovery. Due to lack of research on the dynamically changing environment and detection of chemical substances considering symptoms, real-time knowledge services are required, such as rapid diagnosis of chemicals exposed at the accident site and the following early response. In this study, we propose an AI-based analysis system, Symptom-based Expert for Advanced Response to Chemical Hazards (SEARCH), for chemical substance diagnosis from exposure symptoms actively collected for real-time response and mitigation to hazardous material accidents. Knowledge is collected from chemical database such as WISER, PubChem etc., and integrated for the analytics of chemical exposure accidents and contact symptoms. We design and construct ontology and knowledge graph (KG) for 1001 major chemical substances. The built KG is verified using KG embedding models and the performance of each model is compared. The proposed system identifies the substance candidates through KG query and reasoning considering the exposure conditions. Using the symptom KG, the system SEARCH can provide the means to analyze real-time data from the field and transform it into insights and actions related to emergency response.
ISSN:0957-5820
1744-3598
DOI:10.1016/j.psep.2021.11.002