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数据分析与知识发现  2019, Vol. 3 Issue (3): 45-56    DOI: 10.11925/infotech.2096-3467.2018.0609
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于电商数据的产品知识图谱构建研究*
丁晟春1,2(),侯琳琳1,王颖1
1南京理工大学经济管理学院 南京 210094
2江苏省社会公共安全科技协同创新中心 南京 210094
Product Knowledge Map Construction Based on the E-commerce Data
Shengchun Ding1,2(),Linlin Hou1,Ying Wang1
1School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China
2Jiangsu Collaborative Innovation Center of Social Safety Science and Technology, Nanjing 210094, China
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摘要 

【目的】引入产品画像概念, 解决现有电商平台上对产品静态信息和动态评论关联上的不足。【方法】将知识图谱作为挖掘、组织、存储、展示产品信息的方法, 引入到产品画像的构建研究中, 提出基于知识图谱的产品画像构建方法。【结果】通过设计三项实验生成手机知识图谱数据层, 其中命名实体抽取实验的F值达到77.52%, 评价对象-评价词抽取实验的F值达到76.04%, 同义词发现实验的F值为63.16%。其实验结果验证了所提方法的有效性。【局限】产品画像构建中的关系抽取限定了关系类别, 使得画像中的关系数量有限; 对产品市场流通维度的分析有限。【结论】本研究能够有效帮助购物平台改善产品对比和产品搜索等机制, 为用户提供更好的产品服务。

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丁晟春
侯琳琳
王颖
关键词 产品知识图谱实体抽取评价对象抽取属性抽取实体链接    
Abstract

[Objective] To solve the deficiencies of the static information and dynamic comments on the existing e-commerce platform, the concept of product profile was introduced. [Methods] As a method of mining, organizing, storing and displaying product information, knowledge map is introduced into the research of product profile construction, and a product profile construction method based on knowledge map is proposed. [Results] Three experiments were designed to generate the mobile phone knowledge map data layer, in which the F value of the named entity extraction experiment reached 77.52%, the F value of the evaluation object-evaluation word extraction experiment reached 76.04%, and the F value of the synonym discovery experiment was 63.16%. The experimental results verified the effectiveness of the proposed method. [Limitations] The relationship extraction in product profile construction limits the relationship category, so that the number of relationships in profile is limited; the analysis of the product market circulation dimensions is limited. [Conclusions] This study has effectively helped the shopping platform to improve product comparisons and product search mechanisms to provide users with better products and services.

Key wordsProduct Knowledge Map    Entity Extraction    Opinion Target Extraction    Attribute Extraction    Entity Linking
收稿日期: 2018-06-01     
基金资助:*本文系国家社会科学基金项目“基于社会网络分析的网络舆情主题发现研究”(项目编号: 15BTQ063)和江苏省社会公共安全优势学科项目的研究成果之一
引用本文:   
丁晟春,侯琳琳,王颖. 基于电商数据的产品知识图谱构建研究*[J]. 数据分析与知识发现, 2019, 3(3): 45-56.
Shengchun Ding,Linlin Hou,Ying Wang. Product Knowledge Map Construction Based on the E-commerce Data. Data Analysis and Knowledge Discovery, DOI:10.11925/infotech.2096-3467.2018.0609.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.0609
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