Knowledge-based question answering (KBQA) is an essential but challenging task for artificial intelligence and natural language processing. A key challenge pertains to the design of effective algorithms for relation detection. Conventional methods model questions and candidate relations separately through the knowledge bases (KBs) without considering the rich word-level interactions between them. This approach may result in local optimal results. This article presents a global–local attentive relation detection model (GLAR) that utilizes the local module to learn the features of word-level interactions and employs the global module to acquire nonlinear relationships between questions and their candidate relations located in KBs. This article also reports on the application of an end-to-end retrieval-based KBQA system incorporating the proposed relation detection model. Experimental results obtained on two datasets demonstrated GLAR's remarkable performance in the relation detection task. Furthermore, the functioning of end-to-end KBQA systems was significantly improved through the relation detection model, whose results on both datasets outperformed even state-of-the-art methods. Impact Statement—Knowledge-based question answering (KBQA) aims at answering user questions posed over the knowledge bases (KBs). KBQA helps users access knowledge in the KBs more easily, and it works on two subtasks: entity mention detection and relation detection. While existing relation detection algorithms perform well on the global representation of questions and relations sequences, they ignore some local semantic information on interaction cases between them. The technology proposed in this article takes both global and local interactions into account. With superior improvement on two relation detection tasks and two KBQA end tasks, the technology provides more precise answers. It could be used in more applications, including intelligent customer service, intelligent finance, and others.