Ontology Generation and Instance Extraction of Medicine Information from Thai Semi-structured Data

An ontology is a widely used knowledge base for representing domain knowledge. Developing a knowledge-representing ontology is difficult, as it requires both domain and engineering expertise. Yet, such ontologies are essential for enabling intelligent systems to comprehend real-world knowledge through structured concept networks. In the Thai context, ontology research remains limited due to the scarcity of structured resources, standardized schemas, and annotated corpora for automatic knowledge extraction. This study addresses this gap by proposing a pattern-based methodology for ontology generation and instance extraction from Thai semi-structured medicine data, providing an alternative to resource-intensive deep-learning methods. The proposed approach identifies patterns of collocated Thai text and builds a collocation tree of word sequences, in which shared sequences represent ontological properties and variable sequences represent instance values. The method was applied to two complementary Thai medicine datasets, namely I-Med (a hospital dispensing-record database) and Pobpad (a public health-information website), to generate and integrate ontology components. These templates were transformed into ontological properties and converted into RDF/OWL format to produce a standard ontology usable for querying and reasoning. The generated ontology achieved high performance (Precision = 0.97, Recall = 0.90, F1 = 0.91) and received favorable assessments from domain experts. The results indicate that the proposed approach can effectively extract structured knowledge from Thai semi-structured text and produce a reliable ontology suitable for medical knowledge representation, providing a data-driven foundation for future Thai intelligent systems.