  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 based on graph isomorphisms from basic graph
patterns of SPARQL queries. Learning basic graph patterns is efficient due to
the small number of possible patterns. This novel paradigm reduces the amount
of training data necessary to achieve state-of-the-art performance. TeBaQA also
speeds up the domain adaption process by transforming the QA system development
task into a much smaller and easier data compilation task. In our evaluation,
TeBaQA achieves state-of-the-art performance on QALD-8 and delivers comparable
results on QALD-9 and LC-QuAD v1. Additionally, we performed a fine-grained
evaluation on complex queries that deal with aggregation and superlative
questions as well as an ablation study, highlighting future research
challenges.
