A Semantic Parsing and Reasoning-Based Approach to Knowledge Base Question Answering

Abstract

Knowledge Base Question Answering (KBQA) is a task where existing techniques have faced significant challenges, such as the need for complex question understanding, reasoning, and large training datasets. In this work, we demonstrate DeepThinking Question Answering (DTQA), a semantic parsing and reasoning-based KBQA system.DTQA(1) integrates multiple, reusable modules that are trained specifically for their individual tasks (e.g. semantic parsing, entity linking, and relationship linking), eliminating the need for end-to-end KBQA training data; (2) leverages semantic parsing and a reasoner for improved question understanding. DTQA is a system of systems that achieves state-of-the-art performance on two popularKBQA datasets.