RQE: Rule-Driven Query Expansion to Solve Empty Answers in SPARQL

A branch of question answering approaches translates natural language questions to SPARQL queries. The empty answer problem exists even when we have properly-translated ones, due to the heterogeneity and incompleteness of knowledge graphs. Existing methods use similarities, ontologies or embeddings to relax failed queries and obtain approximate answers, but they may lose efficacy in approximating simple queries with only one or two constraints because of their low accuracy and suitability for over-constrained ones. In this paper, we propose a rule-driven query expansion approach to expand failed queries for obtaining more accurate approximate answers. Specifically, we first automatically build high-quality rule sets for predicates in failed queries with rule learning techniques. Then, we use the learned rules to expand failed queries to get approximate answers and explain the reasons why we choose these answers. We develop two datasets to evaluate the effectiveness and efficiency of our approach and the results show that our approach achieves better results than several approaches based on similarities, ontologies and embeddings in approximating simple queries.