July 1, 2022
Using Ontology-based Text Classification To Assist Job Hazard Analysis
Chi, Nai-wen; Lin, Ken-yu; Hsieh, Shang-hsien. (2014). Using Ontology-based Text Classification To Assist Job Hazard Analysis. Advanced Engineering Informatics, 28(4), 381 – 394.
Abstract
The dangers of the construction industry due to the risk of fatal hazards, such as falling from extreme heights, being struck by heavy equipment or materials, and the possibility of electrocution, are well known. The concept of Job Hazard Analysis is commonly used to mitigate and control these occupational hazards. This technique analyzes the major tasks in a construction activity, identifies all potential task-related hazards, and suggests safe approaches to reduce or avoid each of these hazards. In this paper, the authors explore the possibility of leveraging existing construction safety resources to assist JHA, aiming to reduce the level of human effort required. Specifically, the authors apply ontology-based text classification (TC) to match safe approaches identified in existing resources with unsafe scenarios. These safe approaches can serve as initial references and enrich the solution space when performing JHA. Various document modification strategies are applied to existing resources in order to achieve superior TC effectiveness. The end result of this research is a construction safety domain ontology and its underlying knowledge base. A user scenario is also discussed to demonstrate how the ontology supports JHA in practice. (C) 2014 Elsevier Ltd. All rights reserved.
Keywords
Construction Industry; Health Hazards; Human Factors; Occupational Safety; Ontologies (artificial Intelligence); Pattern Classification; Text Analysis; Ontology-based Text Classification; Job Hazard Analysis; Fatal Hazards; Task-related Hazard; Construction Safety Resource; Jha; Construction Safety Domain Ontology; Construction; Information; Construction Safety; Information Retrieval; Knowledge Management; Ontology; Text Classification
