Knowledge Graph Question Answering Using Graph-Pattern Isomorphism

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 efficient due the small number possible patterns. This paradigm reduces amount data necessary achieve state-of-the-art performance. TeBaQA also speeds up domain adaption process by transforming system development task into much smaller and easier compilation task. our evaluation, achieves performance QALD-8 delivers comparable results QALD-9 LC-QuAD v1. Additionally, performed fine-grained evaluation complex queries deal with aggregation superlative well an ablation study, highlighting future research challenges.