Wang, R.J.R.J.WangWang, M.M.WangLiu, J.J.LiuChen, W.T.W.T.ChenCochez, M.M.CochezDecker, S.S.Decker2022-03-142022-03-142019https://publica.fraunhofer.de/handle/publica/40590110.1007/978-3-030-18576-3_39A promising pathway for natural language question answering over knowledge graphs (KG-QA) is to translate natural language questions into graph-structured queries. During the translation, a vital process is to map entity/relation phrases of natural language questions to the vertices/edges of underlying knowledge graphs which can be used to construct target graph-structured queries. However, due to linguistic flexibility and ambiguity of natural language, the mapping process is challenging and has been a bottleneck of KG-QA models. In this paper, we propose a novel framework, called KemQA, which stands on recent advances in relation phrase dictionaries and knowledge graph embedding techniques to address the mapping problem and construct graph-structured queries of natural language questions. Extensive experiments were conducted on question answering benchmark datasets. The results demonstrate that our framework outperforms state-of-the-art baseline models in terms of effectiveness and efficiency.en004005006conference paper