基于卷积推理的多跳知识图谱问答算法

doi:10.16088/j.issn.1001-6600.2022031703

Abstract

Abstract: Compared with simple questions, multi-hop questions are more in line with people's daily questioning methods. At the same time, the research on the multi-hop knowledge graph question-answering (KGQA) algorithm is useful to enhance the intelligent question answering system. However, the existing multi-hop KGQA methods show weak answer reasoning ability in 2 and 3-hop questions and incomplete knowledge graph. To solve this problem, a multi-hop KGQA based on convolution reasoning is proposed in this paper. A question embedding model combining character features and semantic features is developed according to the semantic similarity between questions and relationships to obtain more expressive question embedding. Furthermore, to enhance the long link reasoning ability of the algorithm, an answer reasoning model based on convolutional neural network (CNN) is proposed to extract the high-order information of the embedding. The experimental results on MetaQA dataset demonstrate that compared with the five existing methods, the new algorithm improves the prediction accuracy of the 2-hop and 3-hop questions in the complete knowledge graph and incomplete knowledge graph by 1.7%, 1.3%, 9.4%, and 9.3%, respectively.

Key words: knowledge graph question-answering, knowledge graph embedding, language model, convolutional neural network

CLC Number:

TP391.1

PAN Haiming, CHEN Qingfeng, QIU Jie, HE Naixu, LIU Chunyu, DU Xiaojing. Multi-hop Knowledge Graph Question Answering Based on Convolution Reasoning[J].Journal of Guangxi Normal University(Natural Science Edition), 2023, 41(1): 102-112.

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Multi-hop Knowledge Graph Question Answering Based on Convolution Reasoning

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