Knowledge Graph Question Answering using Graph-Pattern Isomorphism

Abstract

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.

Version published in the proceedings of the 17th International Conference on Semantic Systems

References in corpus (5)

• KBQA: Learning Question Answering over QA Corpora and Knowledge Bases
• Introduction to Neural Network based Approaches for Question Answering over Knowledge Graphs
• Neural Machine Translating from Natural Language to SPARQL
• Question Answering over Knowledge Graphs via Structural Query Patterns
• Learning to Order Sub-questions for Complex Question Answering

Cited by in corpus (2)

• Modern Baselines for SPARQL Semantic Parsing
• Lexicalization Is All You Need: Examining the Impact of Lexical Knowledge in a Compositional QALD System