Semi-automated Construction of Air Pollution Domain Ontology

Abstract

Air pollution has become a substantial environmental issue in China, which has seriously affected human health. To clarify the relationship among air pollutants, pollution sources, influencing factors, evaluation indicators and harms, it is essential to build a domain ontology for air pollution. Domain ontologies have been gradually accepted as a method to indicate the relationship between terms. However, there is not a complete ontology in air pollution domain, and building domain ontologies manually is time-consuming and inconvenient. In this paper, a semi-automatic approach is presented to build an ontology in air pollution domain. This paper proposes a method of entity relationship joint extraction based on attention mechanism, which is combined with core concept mining method to extract knowledge, and then concepts, relationships, and relevant instances are organized in hierarchy. Finally, the ontology model is constructed semi-automatically and semantic inference is also carried out. The research shows that this knowledge extraction method can avoid the error accumulation caused by entity recognition and relationship extraction and deepen the inner connection between them, and a large amount of effective knowledge in the field of air pollution can be extracted. In addition, the ontology constructed based on this method can also visually analyze the relationship between various classes and concepts in the field of air pollution, deduce the pollutant propagation path, and provide practical experience for the semi-automatic ontology construction in other fields and data support for further research.