Question answering over knowledgebase with attention-based LSTM networks and knowledge embeddings
Liu Chen, Guangping Zeng, Qingchuan Zhang, Xingyu Chen, Danfeng Wu · 2017
With the rapid growth of knowledge bases (KBs), how to take full advantage of structured knowledge infomation becomes increasingly important. Knowledge base-based question answering (KB-QA) is one of the most promising approaches to access the substantial knowledge. Meantime, as the neural network-based (NN-based) methods develop, NN-based KB-QA has already achieved impressive results. However, previous works has limitations which did not express the proper information of the question and take adequate use of knowledge information. Hence, we present a neural attention-based model to represent the questions. In addition, we leverage the global knowledge inside the underlying KB, aiming at integrating the rich KB information into the representation of the answers. The experimental results on WEbQuESTIONS demonstrate the effectiveness of the proposed approach.