  Knowledge base question answering (KBQA)is an important task in Natural
Language Processing. Existing approaches face significant challenges including
complex question understanding, necessity for reasoning, and lack of large
end-to-end training datasets. In this work, we propose Neuro-Symbolic Question
Answering (NSQA), a modular KBQA system, that leverages (1) Abstract Meaning
Representation (AMR) parses for task-independent question understanding; (2) a
simple yet effective graph transformation approach to convert AMR parses into
candidate logical queries that are aligned to the KB; (3) a pipeline-based
approach which integrates multiple, reusable modules that are trained
specifically for their individual tasks (semantic parser, entity
andrelationship linkers, and neuro-symbolic reasoner) and do not require
end-to-end training data. NSQA achieves state-of-the-art performance on two
prominent KBQA datasets based on DBpedia (QALD-9 and LC-QuAD1.0). Furthermore,
our analysis emphasizes that AMR is a powerful tool for KBQA systems.
