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  training datasets. In this work, we propose a semantic parsing and reasoning-based Neuro-Symbolic Question Answering (NSQA)  system, that leverages (1) Abstract Meaning Representation (AMR) parses for task-independent question under-standing ; (2) a novel path-based approach to transform AMR parses into candidate logical queries that are aligned to the KB; (3) a neuro-symbolic reasoner called Logical Neural Net-work (LNN) that executes logical queries and reasons over KB facts to provide an answer; (4) system of systems approach , which integrates multiple, reusable modules that are trained specifically for their individual tasks ( e.g. semantic parsing, entity linking, and relationship linking ) and do not require end-to-end training data. NSQA achieves state-of-the-art performance on  QALD-9 and LC-QuAD 1.0. NSQA's novelty lies in its modular neuro-symbolic architecture and its task-general approach to interpreting natural language questions .
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 .
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end-to-end
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a semantic parsing and reasoning-based
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, a modular KBQA
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under-standing
understanding
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novel path-based approach to transform
simple yet effective graph transformation approach to convert
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neuro-symbolic reasoner called Logical Neural Net-work (LNN) that executes logical queries and reasons over KB facts to provide an answer; (4) system of systems approach ,
pipeline-based approach
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e.g. semantic parsing, entity linking, and relationship linking
semantic parser, entity andrelationship linkers, and neuro-symbolic reasoner
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two prominent KBQA datasets based on DBpedia (
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LC-QuAD 1.0. NSQA's novelty lies in its modular neuro-symbolic architecture and its task-general approach to interpreting natural language questions
LC-QuAD1.0). Furthermore, our analysis emphasizes that AMR is a powerful tool for KBQA systems