Semi-automated Construction of Air Pollution Domain Ontology and Semantic Reasoning

To clarify the relationship among air pollutants, pollution sources, influencing factors, evaluation indicators and harms, and to analyze the air pollution transmission path, a clearer and more complete domain ontology of air pollution was established. First, a method of entity relationship joint extraction based on attention mechanism was proposed. Attention mechanism was added to the model of bi-directional long and short time memory network, and entity and relation were labeled jointly to extract entity relation. Second, it was combined with term frequency-inverse document frequency (TF-IDF) core concept mining method to extract knowledge, and then concepts, relationships, and relevant instances were organized in hierarchy. Furthermore, the ontology model was constructed semi-automatically. Finally, conditional reasoning and visual reasoning were carried out on the basis of ontology and instance through SPARQL Query module and HermiT reasoning machine of Protégé. Results show that the domain ontology of atmospheric pollution constructed by the entity relation extraction method based on attention mechanism contains 68 core entities and 360 instances. Compared with the existing domain ontology, the validity, accuracy and comprehensiveness, reusability of this method have better performance. At the same time, the propagation paths of pollution of ions were deduced. Therefore, the method of sequence labeling and joint extraction of entity relations based on attention mechanism can effectively construct air pollution domain ontology semi-automatically and deduce air pollution propagation path clearly.