Study on direction and strength of relation based on knowledge graph

Genlin ZANG; Yaqiang WANG; Qingrong WU; Chunli ZHAN; Xinyang XIE

doi:10.11959/j.issn.2096-0271.2019027

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Study on direction and strength of relation based on knowledge graph

STUDY | 更新时间：2024-06-03

- Study on direction and strength of relation based on knowledge graph
- Big Data Research Vol. 5, Issue 3, Pages: 2019027-1(2019)
- 作者机构：
  
  1. 拓尔思知识图谱研究院，广东广州 510665
  2. 广州拓尔思大数据有限公司，广东广州 510665
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2019027
  CLC： TP31
- Online First：2019-05，
  
  Published：15 May 2019
- 稿件说明：
移动端阅览
Genlin ZANG, Yaqiang WANG, Qingrong WU, et al. Study on direction and strength of relation based on knowledge graph[J]. Big Data Research, 2019, 5(3): 2019027-1.
DOI：

Genlin ZANG, Yaqiang WANG, Qingrong WU, et al. Study on direction and strength of relation based on knowledge graph[J]. Big Data Research, 2019, 5(3): 2019027-1. DOI： 10.11959/j.issn.2096-0271.2019027.

摘要

目前普遍的知识图谱构建思路是图谱中的关系标签采用文字描述，这样很难对图谱中的关系进行计算。针对这个问题，提出了关系方向、强度因子和时态因子的概念，关系的正负、强度和时态可以通过有监督机器学习的方法形成自动模型，从而在领域知识图谱中实现关系的量化计算。这种知识图谱构建方法在计算事件舆情走向、计算企业合作与竞争情况变化、分析销售人员市场拓展情况等领域，形成了一种新的数据分析模式，对人工智能在具体行业的落地应用很有意义。

Abstract

In current popular ideas for knowledge graph construction

the relations in graphs were described by words

it is difficult to calculate the relations in graphs.To this issue

concepts of the direction

intensive factors

temporal factors of relations were proposed.Automatic models of positive

negative

intensive and temporal relations can be formed through supervised machine learning

so that the quantitative calculation of the relations can be implemented in the domain knowledge graph.This method forms a new idea in many areas such as calculating the trend of incidents

calculating the change of cooperation and competition between enterprises

and analyzing the market expansion of sales people.It is meaningful for artificial intelligence to be applied in specific industries.

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Keywords

references

赵军 , 刘康 , 何世柱 , 等 . 知识图谱 [M ] . 北京 : 高等教育出版社 , 2018 .

ZHAO J , LIU K , HE S Z , et al . Knowledge graph [M ] . Beijing : Higher Education PressPress , 2018 .

陈秀娟 , 冷德荣 . 面向语言信息处理的语义研究——合著者的社会网络分析 [J ] . 情报科学 , 2013 ( 7 ): 126 - 129 .

CHEN X J , LENG D R . Semantic study in language and information processing based on social network analysis [J ] . Information Science , 2013 ( 7 ): 126 - 129 .

秦长江 , 侯汉清 . 知识图谱——信息管理与知识管理的新领域 [J ] . 大学图书馆学报 , 2009 ( 1 ): 30 - 37 ,96.

QIN C J , HOU H Q . Knowledge graph:a new area of information management and knowledge management [J ] . Journal of Academic Libraries , 2009 ( 1 ): 30 - 37 ,96.

徐增林 , 盛泳潘 , 贺丽荣 , 等 . 知识图谱技术综述 [J ] . 电子科技大学学报 , 2016 ( 4 ): 589 - 606 .

XU Z L , SHENG Y P , HE L R , et al . Review on knowledge graph techniques [J ] . Journal of University of Electronic Science and Technology of China , 2016 ( 4 ): 589 - 606 .

李涓子 , 侯磊 . 知识图谱研究综述 [J ] . 山西大学学报（自然科学版） , 2017 , 40 ( 3 ): 454 - 459 .

LI J Z , HOU L . Reviews on knowledge graph research [J ] . Journal of Shanxi University(Natural Science Edition) , 2017 , 40 ( 3 ): 454 - 459 .

朱木易洁 , 鲍秉坤 , 徐常胜 . 知识图谱发展与构建的研究进展 [J ] . 南京信息工程大学学报（自然科学版） , 2017 , 9 ( 6 ): 575 - 582 .

ZHU M Y J , BAO B K , XU C S . Research progress on development and construction of knowledge graph [J ] . Journal of Nanjing University of Information Science ＆Technology (Natural Science Edition) , 2017 , 9 ( 6 ): 575 - 582 .

SOWA J F . Principles of semantic networks:exploration in the representation of knowledge [J ] . Frame Problem in Artificial Intelligence , 1991 ( 23 ): 135 - 157 .

STAAB S , STUDER R , SCHNURR H . Knowledge processes and ontologies [J ] . IEEE Intelligent Systems,Special Issue on Knowledge Management , 2001 , 16 ( 1 ): 26 - 34 .

HENDRICKX I , KIM S N , KOZAREVA Z , et al . SemEval-2010 task 8:multi-way classification of semantic relations between pairs of nominals [C ] // The Workshop on Semantic Evaluations:Recent Achievements and Future Directions,June 4,2009,Boulder,USA.[S.l . ]:Association for Computational Linguistics , 2009 : 94 - 99 .

HASHIMOTO K , STENETORP P , MIWA M , et al . Task-oriented learning of word embeddings for semantic relation classification [J ] . Computer Science , 2015 : 268 - 278 .

ZHOU Z Q , QI G L , GLIMM B . Exploring parallel tractability of ontology materialization [C ] // European Conference on Artificial Intelligence,Hague,Netherlands,August 29-September 2,2016.[S.l:s.n . ] , 2016 : 73 - 81 .

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