  Knowledge Base Question Answering (KBQA) aims to answer natural language
questions with the help of an external knowledge base. The core idea is to find
the link between the internal knowledge behind questions and known triples of
the knowledge base. Traditional KBQA task pipelines contain several steps,
including entity recognition, entity linking, answering selection, etc. In this
kind of pipeline methods, errors in any procedure will inevitably propagate to
the final prediction. To address this challenge, this paper proposes a Corpus
Generation - Retrieve Method (CGRM) with Pre-training Language Model (PLM) for
the KBQA task. The major novelty lies in the design of the new method, wherein
our approach, the knowledge enhanced T5 (kT5) model aims to generate natural
language QA pairs based on Knowledge Graph triples and directly solve the QA by
retrieving the synthetic dataset. The new method can extract more information
about the entities from PLM to improve accuracy and simplify the processes. We
test our method on NLPCC-ICCPOL 2016 KBQA dataset, and the results show that
our method improves the performance of KBQA and the out straight-forward method
is competitive with the state-of-the-art.
