Multi-aspect attentive text representations for simple question answering over knowledge base

With the deepening of knowledge base research and application, question answering over knowledge base, also called KBQA, has recently received more and more attention from researchers. Most previous KBQA models focus on mapping the input query and the fact in KBs into an embedding format. Then the similarity between the query vector and the fact vector is computed eventually. Based on the similarity, each query can obtain an answer representing a tuple (subject, predicate, object) from the KBs. However, the information about each word in the input question will lose inevitably during the process. To retain as much original information as possible, we introduce an attention-based recurrent neural network model with interactive similarity matrixes. It can extract more comprehensive information from the hierarchical structure of words among queries and tuples stored in the knowledge base. This work makes three main contributions: (1) A neural network-based question-answering model for the knowledge base is proposed to handle single relation questions. (2) An attentive module is designed to obtain information from multiple aspects to represent queries and data, which contributes to avoiding losing potentially valuable information. (3) Similarity matrixes are introduced to obtain the interaction information between queries and data from the knowledge base. Experimental results show that our proposed model performs better on simple questions than state-of-the-art in several effectiveness measures.