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Knowledge graph question answering (KGQA) facilitates information access byleveraging structured data without requiring formal query language expertisefrom the user. Instead, users can express their information needs by simplyasking their questions in natural language (NL). Datasets used to train KGQAmodels that would provide such a service are expensive to construct, both interms of expert and crowdsourced labor. Typically, crowdsourced labor is usedto improve template-based pseudo-natural questions generated from formalqueries. However, the resulting datasets often fall short of representinggenuinely natural and fluent language. In the present work, we investigate waysto characterize and remedy these shortcomings. We create the IQN-KGQA testcollection by sampling questions from existing KGQA datasets and evaluatingthem with regards to five different aspects of naturalness. Then, the questionsare rewritten to improve their fluency. Finally, the performance of existingKGQA models is compared on the original and rewritten versions of the NLquestions. We find that some KGQA systems fare worse when presented with morerealistic formulations of NL questions. The IQN-KGQA test collection is aresource to help evaluate KGQA systems in a more realistic setting. Theconstruction of this test collection also sheds light on the challenges ofconstructing large-scale KGQA datasets with genuinely NL questions.

Would You Ask it that Way? Measuring and Improving Question Naturalness for Knowledge Graph Question Answering