  The European Space Agency is well known as a powerful force for scientific
discovery in numerous areas related to Space. The amount and depth of the
knowledge produced throughout the different missions carried out by ESA and
their contribution to scientific progress is enormous, involving large
collections 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 a
hub 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 requiring
automation. In this paper, we present a methodological framework based on
artificial intelligence and natural language processing and understanding to
automatically extract information from Space documents, generating value from
it, and illustrate such framework through several case studies implemented
across different functional areas of ESA, including Mission Design, Quality
Assurance, Long-Term Data Preservation, and the Open Space Innovation Platform.
In doing so, we demonstrate the value of these technologies in several tasks
ranging from effortlessly searching and recommending Space information to
automatically determining how innovative an idea can be, answering questions
about Space, and generating quizzes regarding quality procedures. Each of these
accomplishments represents a step forward in the application of increasingly
intelligent AI systems in Space, from structuring and facilitating information
access to intelligent systems capable to understand and reason with such
information.
