The purpose of knowledge-based question answering (KBQA) is to accurately answer the questions raised by users through knowledge triples. Traditional Chinese KBQA methods rely heavily on artificial features, resulting in unsatisfactory QA results. To solve the above problems, this paper divides Chinese KBQA into two parts: entity extraction and attribute mapping. In the entity extraction stage, the improved Bi-LSTM-CNN-CRF model is used to identify the entity of questions and the Levenshtein distance method is used to resolve the entity link error. In the attribute mapping stage, according to the characteristics of questions and candidate attributes, the MGBA-LSTM-CNN model is proposed to encode questions and candidate attributes from the semantic level and word level, respectively, and splice them into new semantic vectors. Finally, the cosine distance is used to measure the similarity of the two vectors to find candidate attributes most similar to questions. The experimental results show that the system achieves good results in the Chinese question and answer data set.

中文翻译：

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基于 MGBA-LSTM-CNN 模型的自动中文知识问答

基于知识的问答（KBQA）的目的是通过知识三元组准确回答用户提出的问题。传统的中文 KBQA 方法严重依赖人工特征，导致 QA 结果不尽如人意。针对上述问题，本文将中文KBQA分为实体抽取和属性映射两部分。在实体抽取阶段，采用改进的Bi-LSTM-CNN-CRF模型对问题实体进行识别，并采用Levenshtein距离法解决实体链接错误。在属性映射阶段，根据问题和候选属性的特点，提出了MGBA-LSTM-CNN模型，分别从语义层面和词层面对问题和候选属性进行编码，拼接成新的语义向量。最后，余弦距离用于衡量两个向量的相似性，以找到与问题最相似的候选属性。实验结果表明，该系统在中文问答数据集上取得了较好的效果。