# Knowledge Base Question Answering for Space Debris Queries Source: [Knowledge Base Question Answering for Space Debris Queries]() Collection index: [llms.txt]() Data updated: 2026-01-02T02:50:42.686Z Authors: Paul Darm, Antonio Valerio Miceli-Barone, Shay B. Cohen, Annalisa Riccardi Published: 2023-05-31T10:55:41.000Z ## Abstract 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}. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Knowledge Base Question Answering on DISCOS KB (val)](): Knowledge Base Question Answering; DISCOS KB (val); Accuracy (Functions): 79. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()