电信运营商知识图谱技术体系研究及应用实践

doi:10.11959/j.issn.1000-0801.2022145

电信科学 ›› 2022, Vol. 38 ›› Issue (8): 151-162.doi: 10.11959/j.issn.1000-0801.2022145

电信运营商知识图谱技术体系研究及应用实践

赵东明

中国移动通信集团天津有限公司，天津 300020

修回日期:2022-06-27 出版日期:2022-08-20 发布日期:2022-08-01
作者简介:赵东明（1984- ），男，博士，中国移动通信集团天津有限公司正高级工程师、AI实验室负责人，主要研究方向为知识图谱、情感计算及认知网络
基金资助:
中国移动战略级重大研发计划项目：人工智能关键核心技术攻关(R222111101021D5)

Research and application practice of knowledge graph technology system for telecom-operators

Dongming ZHAO

China Mobile Communications Tianjin Co., Ltd., Tianjin 300020, China

Revised:2022-06-27 Online:2022-08-20 Published:2022-08-01
Supported by:
China Mobile Strategic Major Research and Development Plan Project: Key Technology Research in Artificial Intelligence(R222111101021D5)

摘要/Abstract

摘要：

摘要：知识图谱在电信运营商业务及运维场景的知识搜索、工单处理、智能服务、故障分析等领域发挥着重要作用，以中国移动业务运营场景为依托，体系化地研究了知识图谱技术体系，基于应用场景、服务对象可分为问答检索类知识图谱、工单分析类知识图谱、系统运维类知识图谱和业务运营类知识图谱，并介绍其技术架构、业务架构及构建方法，引出知识图谱演进与发展的规划目标。

关键词: 知识图谱, 认知网络, 智能问答, 信息检索, 智能运维

Abstract:

Knowledge graph plays an important role in knowledge search, work order processing, intelligent service, fault analysis and other fields of China Mobile business and maintenance scenario.The knowledge graph technology system of China Mobile was systematically studied.Based on application scenarios and service objects, it can be divided into question ＆amp; answer retrieval knowledge graph, work-order analysis knowledge graph, system operation and maintenance knowledge graph and business operation knowledge graph.The technical architecture, business architecture and construction methods were introduced, and the planning goal of the evolution and development of knowledge graph was ultimately leaded.

Key words: knowledge graph, cognitive network, intelligence question and answer, information retrieval, intelligent operation and maintenance

中图分类号:

TP391.1

赵东明. 电信运营商知识图谱技术体系研究及应用实践[J]. 电信科学, 2022, 38(8): 151-162.

Dongming ZHAO. Research and application practice of knowledge graph technology system for telecom-operators[J]. Telecommunications Science, 2022, 38(8): 151-162.

图/表 6

图1

图2

图3

图4

图5

图6

参考文献 15

[1]	PAN J Z , EDELSTEIN E , BANSKY P ,et al. A knowledge graph based approach to social science surveys[J]. Data Intelligence, 2021,3(4): 477-506.
[2]	HUANG X Q , TANG J Y , TAN Z ,et al. Knowledge graph embedding by relational and entity rotation[J]. Knowledge-Based Systems, 2021(229): 107310.
[3]	ZHENG W B , YAN L , GOU C ,et al. Pay attention to doctor-patient dialogues:Multi-modal knowledge graph attention image-text embedding for COVID-19 diagnosis[J]. Information Fusion, 2021(75): 168-185.
[4]	TOSI M D L , DOS REIS J C . SciKGraph:a knowledge graph approach to structure a scientific field[J]. Journal of Informetrics, 2021,15(1): 101109.
[5]	WANG Z K , LI L J , ZENG D ,et al. Incorporating prior knowledge from counterfactuals into knowledge graph reasoning[J]. Knowledge-Based Systems, 2021(223): 107035.
[6]	SHA X , SUN Z , ZHANG J . Hierarchical attentive knowledge graph embedding for personalized recommendation[J]. Electronic Commerce Research and Applications, 2021(48): 101071.
[7]	LIN Q K , ZHU Y F , LU H ,et al. Improving university faculty evaluations via multi-view knowledge graph[J]. Future Generation Computer Systems, 2021(117): 181-192.
[8]	ABU-SALIH B , AL-TAWIL M ,, ALJARAH I ,et al. Relational learning analysis of social politics using knowledge graph embedding[J]. Data Mining and Knowledge Discovery, 2021,35(4): 1497-1536.
[9]	BUCHGEHER G , GABAUER D , MARTINEZ-GIL J , ,et al. Knowledge graphs in manufacturing and production:a systematic literature review[J]. IEEE Access, 2020: 55537-55554.
[10]	XUE Y Y , JIN J H , SONG A B ,et al. Relation-based multi-type aware knowledge graph embedding[J]. Neurocomputing, 2021(456): 11-22.
[11]	NIU H R , HE H T , FENG J Z ,et al. Knowledge graph completion based on GCN of multi-information fusion and high- dimensional structure analysis weight[J]. Chinese Journal of Electronics, 2022,31(2): 387-396.
[12]	DANG F R , TANG J T , PANG K Y ,et al. Constructing an educational knowledge graph with concepts linked to Wikipedia[J]. Journal of Computer Science and Technology, 2021,36(5): 1200-1211.
[13]	WANG W G , XU Y L , DU C H ,et al. Data set and evaluation of automated construction of financial knowledge graph[J]. Data Intelligence, 2021,3(3): 418-443.
[14]	CHEN Y . Constructing bug knowledge graph as a service for bug search[J]. International Journal of Software Engineering and Knowledge Engineering, 2021,31(1): 81-98.
[15]	PU F , ZHANG Z , FENG Y ,et al. Learning context-based embeddings for knowledge graph completion[J]. Journal of data and Information Science, 2022,7(2): 23-29.

电信运营商知识图谱技术体系研究及应用实践

Research and application practice of knowledge graph technology system for telecom-operators

在线阅读

PDF下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 6

参考文献 15

相关文章 15

Metrics

推荐阅读 0

[1]	张海龙, 李博, 贾娜娟. 基于Dijkstra算法的电力光通信网络智能运维设备脱网故障区段定位方法[J]. 电信科学, 2023, 39(1): 92-99.
[2]	程瑞营, 张攀, 肖雨, 乔宇杰, 张安奕. 基于时序数据的云网协同平台人工智能运维体系[J]. 电信科学, 2022, 38(11): 24-35.
[3]	韩赛, 张冬月, 王泽林, 王光全, 李奥, 方遒铿, 马红兵. 跨专业承载网络智能运维研究与应用[J]. 电信科学, 2022, 38(11): 113-122.
[4]	李艳丽, 林志, 王子宁, 许拔, 程铭, 欧阳键. 基于无人机中继的星地认知网络波束成形算法[J]. 电信科学, 2021, 37(8): 27-37.
[5]	王速, 卢华, 汪硕, 蔡磊, 黄韬. 智能运维中KPI异常检测的研究进展[J]. 电信科学, 2021, 37(5): 42-51.
[6]	刘飞扬, 李坤, 宋飞, 周华春. DDoS攻击恶意行为知识库构建[J]. 电信科学, 2021, 37(11): 17-32.
[7]	陆钢,陈长怡,黄泽龙,黄泽源. 面向云网融合的智能云原生架构和关键技术研究[J]. 电信科学, 2020, 36(9): 67-74.
[8]	薛龙,陆钢,周奇,张会炎,万亭君. 面向云原生的智能运维架构和关键技术[J]. 电信科学, 2020, 36(12): 105-112.
[9]	李青. 基于大数据分析的云资源池告警信息关联方案[J]. 电信科学, 2020, 36(10): 159-171.
[10]	谭晓敏,方艾,梁冰,杨豪杰. 基于决策树的EPG体验异常原因定位[J]. 电信科学, 2019, 35(8): 158-164.
[11]	周晶,孙喜民,于晓昆,边新宁. 知识图谱与数据应用——智能推荐[J]. 电信科学, 2019, 35(8): 165-172.
[12]	谭晓敏,方艾,金铎,李长江. IPTV用户体验异常的自动化检测[J]. 电信科学, 2019, 35(7): 159-164.
[13]	陈天骄,刘江,黄韬. 人工智能在网络编排系统中的应用[J]. 电信科学, 2019, 35(5): 9-16.
[14]	余洋,朱少敏,卞超轶. 基于知识图谱的泛在电力物联网安全可视化技术[J]. 电信科学, 2019, 35(11): 132-139.
[15]	郭云峰,蔡珩,戈磊. 基于大数据网格的电信IT系统智能运维体系[J]. 电信科学, 2018, 34(6): 153-161.