采用语义分析的标签体系构建方法

李娜 1; 范正洁 2; 郝传洲 1; 王平辉 1; 陶敬 1; 林杰 1

doi:10.7652/xjtuxb201901023

您当前的位置：

首页 >

文章列表页 >

采用语义分析的标签体系构建方法

更新时间：2025-07-09

- 采用语义分析的标签体系构建方法
- A Method for Building Tag Systems Based on Semantic Feature Analysis
- 西安交通大学学报 2019年53卷第1期页码：169-174
- 作者机构：
  
  西安交通大学智能网络与网络安全教育部重点实验室,西安,710049
- 作者简介：
- 基金信息：
  
  : 2018-06-08。作者简介: 李娜(1992—),女,硕士生;范正洁(通信作者),女,博士,研究员。基金项目: 深圳基础研究计划资助项目(JCYJ20160229195940462);陕西省自然科学基础研究计划资助项目(2016JQ6034,2017JM6095);浙江省自然科学基金资助项目(LGG18F020016)
- DOI：10.7652/xjtuxb201901023
  中图分类号： TP391.1
- 网络首发：2019-01-10，
  
  纸质出版：2019
- 稿件说明：
移动端阅览
李娜 1, 范正洁 2, 郝传洲 1, 等. 采用语义分析的标签体系构建方法[J]. 西安交通大学学报, 2019,53(1):169-174.

A Method for Building Tag Systems Based on Semantic Feature Analysis[J]. 2019, 53(1): 169-174.
李娜 1, 范正洁 2, 郝传洲 1, 等. 采用语义分析的标签体系构建方法[J]. 西安交通大学学报, 2019,53(1):169-174. DOI： 10.7652/xjtuxb201901023.

A Method for Building Tag Systems Based on Semantic Feature Analysis[J]. 2019, 53(1): 169-174. DOI： 10.7652/xjtuxb201901023.

摘要

针对现有标签体系粒度粗和层级结构不明显的问题

提出了一种基于语义分析的自动化标签体系融合构建方法。该方法通过分析不同网站导航标签体系中标签的相似性

学习不同导航标签间的等同映射关系和上下位映射关系

进而融合不同网站的导航标签体系以得到细粒度且层级分明的标签体系。为了评估该方法在标签体系融合构建方面的精度

首次提出标签重合度和上下位关系重合度两个测试指标进行衡量。实验结果表明

与基于同义词林的标签体系融合构建方法相比

所提方法在标签重合度和上下位关系重合度上提升了5%以上

可以构建出精准有效且适应不同领域的标签体系

为构建精准的用户画像打下基础。

Abstract

An improved method based on semantic features analysis of tag systems is proposed to solve the problem that existing tag systems are coarse-grained and their hierarchical structures are unapparent. The method analyses similarity among multiple tag systems on different websites

learns the relationships of synonym mappings and hypernym-hyponym mappings among these systems

and further obtains a fine-grained and hierarchical tag system. In order to evaluate the performance of the algorithm

two test metrics

the tag coincidence degree and the hypernym-hyponym coincidence degree

are proposed to assess the accuracy of merging and constructing tag systems. Experimental results show that the method improves the tag coincidence degree and the hypernym-hyponym coincidence degree by more than 5%

and it efficiently builds a tag system with higher precision and applicability

meanwhile

lays a foundation for establishing user profile.

关键词

Keywords

references

王元卓, 靳小龙, 程学旗. 网络大数据: 现状与展望 [J]. 计算机学报, 2013, 36(6): 1125-1138.

WANG Yuanzhuo, JIN Xiaolong, CHENG Xueqi. Network big data: present and future [J]. Chinese Journal of Computers, 2013, 36(6): 1125-1138.

刘海, 卢慧, 阮金花, 等. 基于“用户画像”挖掘的精准营销细分模型研究 [J]. 丝绸, 2015, 52(12): 37-42.

LIU Hai, LU Hui, RUAN Jinhua, et al. Research on precision marketing segmentation model based on mining “persona” [J]. Journal of Silk, 2015, 52(12): 37-42.

刘光榕, 任建涛. 基于客户画像的精准推荐 [J]. 科技经济导刊, 2016(35): 22, 78.

高寅欣, 郭中梅. 运营商移动互联网用户大数据分析及应用 [J]. 中国电信业, 2015(9): 80-83.

GAO Yinxin, GUO Zhongmei. Analysis and application of large data of mobile internet users [J]. China Telecommunication Trade, 2015(9): 80-83.

王巍. 用户标签在基于用户画像的精准营销中的应用解析 [J]. 经济管理(全文版), 2016(8): 00232.

WANG Wei. Application of user tag in precision marketing based on user portrait [J]. Economic Management Journal, 2016(8): 00232.

GOLDER S A, HUBERMAN B A. Usage patterns of collaborative tag systems [J]. Journal of Information Science, 2006, 32(2): 198-208.

王翠英. 基于Folksonomies的用户偏好挖掘研究 [J]. 现代图书情报技术, 2009, 25(6): 37-43.

WANG Cuiying. Research development of Folksonomies based user profiles mining [J]. New Technology of Library and Information Service, 2009, 25(6): 37-43.

TAHAR-RAFIK B, RACHID A O. Towards a new approach for generating user profile from folksonomies [C]∥Proceedings of the 2014 4th International Symposium ISKO-Maghreb: Concepts and Tools for Knowledge Management. Piscataway, NJ, USA: IEEE, 2014: 1-6.

CAI Yi, LI Qing, XIE Haoren, et al. Exploring personalized searches using tag-based user profiles and resource profiles in folksonomy [J]. Neural Network, 2014, 58(10): 98-110.

CAI Shubin, SUN Heng, GU Sishan, et al. Learning concept hierarchy from folksonomy [C]∥Proceedings of the 2011 8th Web Information Systems and Applications Conference. Piscataway, NJ, USA: IEEE, 2011: 47-51.

SKILLEN K L, NUGENT C, DONNELLY M, et al. Using ontologies for managing user profiles in personalised mobile service delivery [M]∥ Health Monitoring and Personalized Feedback Using Multimedia Data. Berlin, Germany: Springer, 2015: 245-264.

MALESZKA B. A method for ontology-based user profile adaptation in personalized document retrieval systems [C]∥Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics. Piscataway, NJ, USA: IEEE, 2017: 003187-003192.

FERREIRA-SATLER M, ROMERO F P, OLIVAS J A, et al. Fuzzy ontology-based approach for automatic construction of user profiles [C]∥Proceedings of the International Conference on Rough Sets and Current Trends in Computing. Berlin, Germany: Springer, 2014: 339-346.

BLEI D M, NG A Y, JORDAN M I. Latent Dirichlet allocation [J]. Journal of Machine Learning Research, 2003, 3: 993-1022.

FARSEEV A, NIE L, AKBARI M, et al. Harvesting multiple sources for user profile learning: a big data study [C]∥Proceedings of the ACM International Conference on Multimedia Retrieval. New York, USA: ACM, 2015: 235-242.

LE Q, MIKOLOV T. Distributed representations of sentences and documents [C]∥Proceedings of the International Conference on Machine Learning. Freiburg, Germany: IMLS, 2014: 2931-2939.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

混合电解槽阵列协同的风光耦合制氢系统双层多目标优化

结构-语义-残差协同的图像压缩框架：面向隐私保护与高保真重建

电子级超纯氮深冷空分流程节能优化研究

EBSILON软件在热电联产系统优化中的应用综述

面向高海拔空冷发电机防晕性能演化规律的电热耦合研究