KBQA is a task that requires to answer questions by using semantic structured information in knowledge base. Previous work this area has been restricted due the lack of large parsing dataset and exponential growth searching space with increasing hops relation paths. In paper, we propose an efficient pipeline method equipped pretrained language model. By adopting beam search algorithm, will not ...

Knowledge Graph Question Answering (KGQA) systems are based on machine learning algorithms, requiring thousands of question-answer pairs as training examples or natural language processing pipelines that need module fine-tuning. In this paper, we present a novel QA approach, dubbed TeBaQA. Our approach learns to answer questions graph isomorphisms from basic patterns SPARQL queries. Learning is...