[1]赖佳扬,张晓滨,马瑛超.基于知识图谱的服装问答系统[J].计算机技术与发展,2023,33(02):99-104.[doi:10. 3969 / j. issn. 1673-629X. 2023. 02. 015]
　LAI Jia-yang,ZHANG Xiao-bin,MA Ying-chao.Clothing Question Answering System Based on Knowledge Graph[J].,2023,33(02):99-104.[doi:10. 3969 / j. issn. 1673-629X. 2023. 02. 015]
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基于知识图谱的服装问答系统()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
33
期数:
2023年02期
页码:
99-104
栏目:
软件技术与工程
出版日期:
2023-02-10

文章信息/Info

Title:
Clothing Question Answering System Based on Knowledge Graph
文章编号:
1673-629X(2023)02-0099-06
作者:
赖佳扬; 张晓滨; 马瑛超
西安工程大学 计算机科学学院,陕西 西安 710048
Author(s):
LAI Jia-yang; ZHANG Xiao-bin; MA Ying-chao
School of Computer Science,Xi’an Polytechnic University,Xi’an 710048,China
关键词:
服装; 知识图谱; 知识问答; 知识抽取; Bert 预训练模型
Keywords:
clothing; knowledge graph; question answer; knowledge extraction; Bert pre-training model
分类号:
TP391. 1
DOI:
10. 3969 / j. issn. 1673-629X. 2023. 02. 015
摘要:
随着电商产业的不断发展,消费者希望在网上购物时能够和商家进行更好的沟通,而人工客服需要浪费大量的人力物力。 为合理、有效地利用服装资源,文章通过构建服装知识图谱,并基于知识图谱实现服装知识自动问答。 该问答系统利用目标实体的多跳关系与问句进行匹配从而完成答案生成,知识问答模型采用 BERT 作为编码层,使用 LSTM 网络对知识库的多跳关系进行学习,利用自注意力机制对知识库特征和问句特征进行计算,最终通过二分类的输出将问句和知识库的匹配结果作为评分,并根据评分给出答案。 文章对问答系统的准确性和运行效率在自建的服装数据上进了实验。 实验表明,该问答方法相对于传统的答案匹配方法效果更好,同时在运行效率的实验上验证了方法在实际中的可行性。 基于知识图谱进行问答系统的搭建可以有效地解答消费者在服装知识和服装搭配推荐上的问题,在提高用户体验的同时节约了人力资源。
Abstract:
With the continuous development of the e-commerce industry,consumers hope to have better communication with merchantswhen shopping online, and manual customer service requires a lot of waste of manpower and material resources. In order to makereasonable and effective use of clothing resources,we construct clothing knowledge graph and realize automatic question answering ofclothing knowledge based on the knowledge graph. The question answering system uses the multi-hop relationship of the target entity tomatch the question sentence to complete the answer generation. The knowledge question answering model uses BERT as the encodinglayer,uses the LSTM network to learn the multi - hop relationship of the knowledge base, and uses the self - attention mechanism tocalculate the feature of knowledge base and the feature of the question base. Finally,the matching result of the question base and theknowledge base is scored through the binary output,and the answer is given according to the score. The article conducts experiments onthe accuracy and operation efficiency of the question answering system on the self - built clothing data. Experiments show that theproposed question answering method has better effect than the traditional answer matching method,and its feasibility in practice is verifiedin the experiment of operation efficiency. The construction of question answering system based on knowledge graph can effectivelyanswer consumers爷 questions about clothing knowledge and clothing matching recommendation,which can improve user experience andsave human resources.

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更新日期/Last Update: 2023-02-10