Question Answering intends to automatically extract accurate and relevant information as the answer to a particular question. A large amount of data from the Web is stored as Knowledge bases in a structured way. Question answering on Knowledge bases is a research field that involves multiple branches of computer science like natural language processing, information retrieval and artificial intelligence. Knowledge Base Question Answering (KBQA) research involves various challenges to be solved in multiple aspects. This thesis aimed to compare several state-of-the-art methods for single relation KBQA. The widely used standard single relation dataset, SimpleQuestions dataset was used in the study against Freebase Knowledge Base (KB). A comprehensive analysis of the underlying models and their architecture was performed. Furthermore, to identify the drawbacks and possible enhancements, several approaches for evaluating the models were explored. The results show how the models were performed and the suitability of considering them for solving real-world problems in question answering.