TY - JOUR
T1 - Graph-based intelligent accident hazard ontology using natural language processing for tracking, prediction, and learning
AU - Hong, Eunbin
AU - Lee, Seung Yeon
AU - Kim, Hayoung
AU - Park, Jeong Eun
AU - Seo, Myoung Bae
AU - Yi, June Seong
N1 - Publisher Copyright:
© 2024 Elsevier B.V.
PY - 2024/12/1
Y1 - 2024/12/1
N2 - This paper addresses the challenge of dispersed accident-related information on construction sites, which hinders consensus among employers, workers, supervisors, and society. A robust NLP-based framework is presented to analyze and structure accident-related textual data into a comprehensive knowledge base that reveals accident patterns and risk information. Accident scenarios, including frequency and severity scores, are structured into a graph database through knowledge modeling, establishing an ontology to elucidate keyword relationships. Network analysis identifies accident patterns, quantifies scenario likelihood and severity, and predicts criticality, forming an accident hazard ontology. This vectorized ontology supports accident tracking, prediction, and learning with potential applications. The framework ensures reliable data integration, real-time hazard assessment, and proactive safety measures.
AB - This paper addresses the challenge of dispersed accident-related information on construction sites, which hinders consensus among employers, workers, supervisors, and society. A robust NLP-based framework is presented to analyze and structure accident-related textual data into a comprehensive knowledge base that reveals accident patterns and risk information. Accident scenarios, including frequency and severity scores, are structured into a graph database through knowledge modeling, establishing an ontology to elucidate keyword relationships. Network analysis identifies accident patterns, quantifies scenario likelihood and severity, and predicts criticality, forming an accident hazard ontology. This vectorized ontology supports accident tracking, prediction, and learning with potential applications. The framework ensures reliable data integration, real-time hazard assessment, and proactive safety measures.
KW - Construction safety management
KW - Hazard analysis
KW - Knowledge modeling
KW - Natural language processing (NLP)
KW - Network analysis
KW - Ontology of intelligence
KW - Relation extraction
KW - Safety risk
KW - Weighted graph database
UR - http://www.scopus.com/inward/record.url?scp=85205549347&partnerID=8YFLogxK
U2 - 10.1016/j.autcon.2024.105800
DO - 10.1016/j.autcon.2024.105800
M3 - Article
AN - SCOPUS:85205549347
SN - 0926-5805
VL - 168
JO - Automation in Construction
JF - Automation in Construction
M1 - 105800
ER -