Innovative High Quality Aircraft Maintenance by Wisdom of Semantic Database Using Historical Data of Operation Staffs

Airlines' safety records and maintenance management are some of the most important factors for the passengers to select their preferred airlines. Airlines with amicable aircraft safety and maintenance records employ appropriate maintenance policies and strive to retain veteran maintenance staff. But due to high growth in aviation industry, airlines are now facing shortage of experienced staff and as a result new and less experienced staff is assigned much crucial aircraft maintenance activities. Airline operators have realized that in order to ensure high safety record, they must have high quality maintenance system and documents with highly experienced maintenance staff. To achieve these goals, this paper proposes wisdom of semantic database using historical data from maintenance operation of staff. The wisdom is generated by operators' daily maintenance documents in each stage. We have designed Ontology Semantic Classify Rule Engine and utilized Natural Language Processing (NLP) and Disambiguation technique to classify the meaning of operations.