A Question Answering Method of Knowledge Graph Based on BiLSTM-CRF and Seq2Seq

• Published in: 2022 11th International Conference of Information and Communication Technology (ICTech) pp. 1 - 5
• Main Authors: Zhang, Yiying, Ma, Caixia, He, Yeshen, Liang, Kun, Wu, Yannian, Liu, Zhu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.02.2022
• Subjects: Analytical models; BiLSTM-CRF model; entity recognition; Fitting; intelligent Q&A; Knowledge based systems; Knowledge engineering; knowledge graph; model building; Neural networks; Question answering (information retrieval); Semantics
• DOI: 10.1109/ICTech55460.2022.00017

In natural language processing, intelligent question answering based on knowledge graph has received great attention. In the previous knowledge base question answering, the traditional word vector is difficult to express the text semantic information, and the cyclic neural network is easy to cause gradient disappearance and gradient explosion. At the same time, it is lack of comprehensive consideration of text context information. This paper proposes an intelligent Q & A method based on knowledge graph, which uses BiLSTM-CRF model to realize entity recognition. The intelligent Q & A model is constructed based on Seq2Seq, and the above methods are verified by taking the intelligent Q & A as an example, which effectively improves the accuracy of intelligent Q & A.