融合事实文本的知识库问答方法

doi:10.16451/j.cnki.issn1003-6059.202103008

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Abstract In natural language problems, the relationship expression in the knowledge base is diversified. Therefore, matching the answers of the knowledge base question and answer through representation learning is still a challenge. To make up the shortcomings, a knowledge base question answering method incorporating fact text is proposed. Entities, entity types and relationships in the knowledge base are converted into fact text. A pre-trained language model(BERT) is employed for representation. The vector of question and answers in low dimensional semantic space is obtained using the rich semantic mode of BERT. The answer with the closest semantic similarity to the question is matched by calculation. Experiments show that the proposed method is effective and robust in answering common simple questions.

Key words： Question Answering System Knowledge Base Representation Learning Deep Learning

Received: 27 January 2021

ZTFLH:

TP 391

Fund:National Key Research and Development Program of China(No.2018YFC0830101,2018YFC0830105,2018YFC0830100), National Natural Science Foundation of China(No.61533018,61972186,61762056,61472168,61702512), Yunnan High-tech Industry Development Project(No.201606)

Corresponding Authors: YU Zhengtao, Ph.D., professor. His research interests include natural language processing, information retrieval and machine translation.

About author:: WANG Guangxiang, master student. His research interests include natural language pro-cessing.HE Shizhu, Ph.D., associate professor. His research interests include knowledge graph and natural language processing.LIU Kang, Ph.D., professor. His research interests include knowledge graph and natural language processing.GAO Shengxiang, Ph.D., associate professor. Her research interests include natural language processing.GUO Junjun, Ph.D., associate professor. His research interests include natural language processing.

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	GUO Junjun

Cite this article:

WANG Guangxiang,HE Shizhu,LIU Kang等. Knowledge Base Question Answering Method Incorporating Fact Text[J]. , 2021, 34(3): 267-274.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202103008 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2021/V34/I3/267

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