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Description
This article presents a solution for space industry engineers: an AI-powered virtual assistant. With Natural Language Processing (NLP) and knowledge graphs, the virtual assistant can provide answers with meaningful insights to both technical and non-technical questions by searching through handbooks and standards, increasing efficiency and improving knowledge transfer accuracy. The model is trained using sample data sets from the European Space Agency (ESA) through the development of data scraping scripts. Once its quality has been evaluated and approved by engineers, the model can be further trained with non-public data for local use on Ethernet servers. The article details the applications of NLP and knowledge graphs in the space industry, including knowledge management, system design support, requirement specification, and troubleshooting. It also discusses the challenges and opportunities of using NLP and knowledge graphs in the space industry, such as handling multi-lingual and technical language, integrating disparate data sources, and managing uncertainties and errors. Finally, the article concludes with information on the proposed system architecture and real-life use cases from a demo version of the model, trained on ESA sample documents with real-life problems.
