A method of generating questions by combining a triple and an entity type in a knowledge base. An input of a neural network model is a word vector sequence representing a reconstructed triple, and an output, obtained by processing the word vector sequence, is a word vector sequence used to represent a question. The present method comprises: building a new triple on the basis of entity types corresponding to a head entity and a tail entity in a triple; using the GloVe word embedding method to obtain a representation of the new triple and a question corresponding to the new triple; using an attention mechanism-based gating mechanism recurrent neural network to encode the representation of the new triple, and outputting a representation of a triple incorporating contextual information; using the attention mechanism-based gating mechanism recurrent neural network to decode the representation of the triple, and outputting a representation of the question corresponding to the triple, thereby outputting the question; and replacing the entity types in the question output by the model with specific entities to obtain a new question. In the present method, the triple is combined with information of entity types corresponding to the head entity and the tail entity in the triple in a knowledge base and, by means of the attention mechanism-based neural network model, a question more grammatically fluent and more related to the input triple is obtained.
METHOD OF GENERATING QUESTIONS BY COMBINING TRIPLE AND ENTITY TYPE IN KNOWLEDGE BASE