Knowledge Base Question Answering for Space Debris Queries
Paul Darm Antonio Valerio Miceli-Barone Shay B. Cohen Annalisa Riccardi
Space agencies execute complex satellite operations that need to be supported
by the technical knowledge contained in their extensive information systems.
Knowledge bases (KB) are an effective way of storing and accessing such
information at scale. In this work we present a system, developed for the
European Space Agency (ESA), that can answer complex natural language queries,
to support engineers in accessing the information contained in a KB that models
the orbital space debris environment. Our system is based on a pipeline which
first generates a sequence of basic database operations, called a %program
sketch, from a natural language question, then specializes the sketch into a
concrete query program with mentions of entities, attributes and relations, and
finally executes the program against the database. This pipeline decomposition
approach enables us to train the system by leveraging out-of-domain data and
semi-synthetic data generated by GPT-3, thus reducing overfitting and shortcut
learning even with limited amount of in-domain training data. Our code can be
found at \url{https://github.com/PaulDrm/DISCOSQA}.