基于BiLSTM-CRF的细粒度知识图谱问答

基于知识图谱的问答中问句侯选主实体筛选步骤繁琐,且现有多数模型忽略了问句与关系的细粒度相关性。针对该问题,构建基于BiLSTM-CRF的细粒度知识图谱问答模型,其中包括实体识别和关系预测2个部分。在实体识别部分,利用BiLSTM-CRF模型提高准确性,并将N-Gram算法与Levenshtein距离算法相结合用于候选主实体的筛选,简化候选主实体筛选过程。在关系预测部分,分别应用注意力机制和卷积神经网络从语义层次和词层次捕获问句与关系之间的相互联系。使用FreeBase中的FB2M和FB5M评估数据集进行实验,结果表明,与针对单一关系的问答方法相比,该模型对于实体关系对的预测准确率更高。

Fine-grained Question Answering over Knowledge Graph Based on BiLSTM-CRF

Question answering over knowledge graph is complex in the filtering of candidate master entities of questions,and most existing models ignore the fine-grained correlation between questions and relationships.To address the problem,this paper proposes a fine-grained question answering model over knowledge graph based on BiLSTM-CRF.The model is divided into two parts:entity recognition and relationship prediction.In the entity recognition part,the model uses the BiLSTM-CRF algorithm to improve accuracy,and the N-Gram algorithm is combined with the Levenshtein Distance algorithm to simplify the filtering process of candidate master entities.In the relationship prediction part,attention mechanism and Convolutional Neural Network(CNN)are used to capture the correlation between questions and relationships at the semantic level and the word level.Experimental results on the FB2M and FB5M evaluation datasets in FreeBase show that the proposed model has higher accuracy of entity relationship pair prediction compared with existing question answering methods for a single relationship.