基于多特征语义匹配的知识库问答系统  

Knowledge base question answering system based on multi-feature semantic matching

作　　者：赵小虎[1,2] 赵成龙 ZHAO Xiaohu;ZHAO Chenglong(National and Local Joint Engineering Laboratory of Internet Application Technology on Mine(China University of Mining and Technology),Xuzhou Jiangsu 221008,China;School of Information and Control Engineering,China University of Mining and Technology,Xuzhou Jiangsu 221116,China)

机构地区：[1]矿山互联网应用技术国家地方联合工程实验室(中国矿业大学),江苏徐州221008 [2]中国矿业大学信息与控制工程学院,江苏徐州221116

出　　处：《计算机应用》2020年第7期1873-1878,共6页journal of Computer Applications

基　　金：国家重点研发计划项目(2017YFC0804400)。

摘　　要：知识库问答(KBQA)任务主要目的在于精确地将自然语言问题和知识库(KB)中的三元组进行匹配。传统的KBQA方法通常专注于实体识别和谓语匹配,实体识别的错误会导致错误传播从而无法得到正确的答案。针对上述问题提出一种端到端的解决方案直接匹配问题和三元组,该系统主要包含候选三元组生成和候选三元组排序两个部分来实现精确问答。首先通过BM25算法计算问题和知识库中三元组的相关性生成候选三元组;然后通过多特征语义匹配模型(MFSMM)进行三元组的排序,即用MFSMM分别通过双向长短时记忆网络(Bi-LSTM)和卷积神经网络(CNN)实现语义相似度和字符相似度的计算,并通过融合来对三元组进行排序。该系统在NLPCC-ICCPOL 2016KBQA数据集上的平均F1为80.35%,接近了现有最好的表现。The task of Question Answering over Knowledge Base(KBQA)mainly aims at accurately matching natural language question with triples in the Knowledge Base(KB).However,traditional KBQA methods usually focus on entity recognition and predicate matching,and the errors in entity recognition may lead to error propagation and thus fail to get the right answer.To solve the above problem,an end-to-end solution was proposed to directly match the question and triples.This system consists of two parts:candidate triples generation and candidate triples ranking.Firstly,the candidate triples were generated by the BM25 algorithm calculating the correlation between the question and the triples in the knowledge base.Then,Multi-Feature Semantic Matching Model(MFSMM)was used to realize the ranking of the triples,which means the semantic similarity and character similarity were calculated by MFSMM through Bi-directional Long Short Term Memory Network(Bi-LSTM)and Convolutional Neural Network(CNN)respectively,and the triples were ranked by fusion.With NLPCC-ICCPOL 2016 KBQA as the dataset,the average F1 of the proposed system is 80.35%,which is close to the existing best performance.

关 键 词：知识库 自然语言问题 三元组 多特征语义匹配模型 语义相似度 字符相似度 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]