[Paper Review] Question Answering over Knowledge Bases by Leveraging Semantic Parsing and Neuro-Symbolic Reasoning.

Pavan Kapanipathi, Ibrahim Abdelaziz+28 more|arXiv (Cornell University)|Dec 3, 2020

Topic ModelingComputer Science54 references26 citations

TL;DR

This paper proposes NSQA, a neuro-symbolic question answering system that uses Abstract Meaning Representation (AMR) for semantic parsing, a path-based method to generate logical queries from AMRs, and a Logical Neural Network (LNN) for reasoning over knowledge bases. The modular, task-general architecture achieves state-of-the-art performance on QALD-9 and LC-QuAD 1.0 without end-to-end training.

ABSTRACT

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.

Motivation & Objective

• To address the challenges of complex question understanding and reasoning in knowledge base question answering (KBQA).
• To overcome the lack of large-scale end-to-end training data for KBQA systems.
• To develop a task-general, modular approach that decouples semantic parsing, entity linking, and relationship linking into reusable components.
• To improve reasoning over knowledge bases by integrating neuro-symbolic execution with logical queries derived from AMR parses.
• To achieve state-of-the-art performance on benchmark KBQA datasets without requiring end-to-end fine-tuning.

Proposed method

• Utilizes Abstract Meaning Representation (AMR) parses to enable task-independent understanding of natural language questions.
• Applies a novel path-based approach to transform AMR parses into candidate logical queries aligned with knowledge base schema.
• Employs a Logical Neural Network (LNN) as a neuro-symbolic reasoner to execute logical queries and infer answers from KB facts.
• Designs a system-of-systems architecture that integrates independently trained modules for semantic parsing, entity linking, and relationship linking.
• Avoids end-to-end training by training each module on its specific task, enhancing modularity and reusability.
• Aligns AMR-derived queries with KB schema through path extraction and logical query generation, enabling precise reasoning.

Experimental results

Research questions

• RQ1 Can a modular, neuro-symbolic architecture improve KBQA performance without requiring end-to-end training data?
• RQ2 How effectively can AMR parsing support task-independent question understanding in KBQA?
• RQ3 To what extent does path-based transformation of AMR parses into logical queries enhance reasoning accuracy?
• RQ4 Can a Logical Neural Network (LNN) effectively execute logical queries over knowledge bases to produce accurate answers?
• RQ5 Does the system-of-systems design enable state-of-the-art performance on standard KBQA benchmarks?

Key findings

• NSQA achieves state-of-the-art performance on the QALD-9 benchmark, outperforming prior methods in question answering accuracy.
• The system achieves state-of-the-art results on the LC-QuAD 1.0 benchmark, demonstrating strong generalization across diverse question types.
• The modular design enables high reusability and independent training of components such as semantic parsing, entity linking, and relationship linking.
• The use of AMR parsing enables robust, task-independent question understanding without task-specific fine-tuning.
• The path-based query generation method effectively maps natural language questions to executable logical queries aligned with the knowledge base schema.
• The integration of LNN enables reliable neuro-symbolic reasoning over KB facts, improving answer accuracy through logical consistency.