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The European Space Agency is well known as a powerful force for scientificdiscovery in numerous areas related to Space. The amount and depth of theknowledge produced throughout the different missions carried out by ESA andtheir contribution to scientific progress is enormous, involving largecollections of documents like scientific publications, feasibility studies,technical reports, and quality management procedures, among many others.Through initiatives like the Open Space Innovation Platform, ESA also acts as ahub for new ideas coming from the wider community across different challenges,contributing to a virtuous circle of scientific discovery and innovation.Handling such wealth of information, of which large part is unstructured text,is a colossal task that goes beyond human capabilities, hence requiringautomation. In this paper, we present a methodological framework based onartificial intelligence and natural language processing and understanding toautomatically extract information from Space documents, generating value fromit, and illustrate such framework through several case studies implementedacross different functional areas of ESA, including Mission Design, QualityAssurance, Long-Term Data Preservation, and the Open Space Innovation Platform.In doing so, we demonstrate the value of these technologies in several tasksranging from effortlessly searching and recommending Space information toautomatically determining how innovative an idea can be, answering questionsabout Space, and generating quizzes regarding quality procedures. Each of theseaccomplishments represents a step forward in the application of increasinglyintelligent AI systems in Space, from structuring and facilitating informationaccess to intelligent systems capable to understand and reason with suchinformation.

Artificial Intelligence and Natural Language Processing and Understanding in Space: A Methodological Framework and Four ESA Case Studies