Automating Hazard-Specific Ontology Construction: Methodological Advancements through Ontology Learning Techniques from Disaster-Related Knowledge Bases

The Ontology Learning System (OLS) aims to automate the construction of hazard-specific ontologies, with a particular focus on floods. Using ontology learning techniques and Machine Learning algorithms, OLS undergoes a series of preprocessing steps, including text cleaning, word tokenization, and the removal of stop words and irrelevant characters. Extracting frequent and relevant terms while also classifying disaster-related keywords is a critical component of automating the construction of a flood ontology. Using Bidirectional Encoder Representations from Transformers (BERT), the model achieved 89% accuracy, precision, and recall in classifying keywords as disaster-related or not. Further, the system employs Machine Learning algorithms, namely Naive Bayes, Logistic Regression, and Linear Support Vector Classifier, for multiclass classification of disaster types. The results of the multiclass classification show that the Linear Support Vector Classifier attained the highest accuracy. The identified ontology keywords can be used to construct a flood-specific ontology, playing a crucial role in organizing flood-related information. Integrating these ontologies into broader systems can significantly enhance disaster preparedness and improve overall response capabilities.