知识图谱中的关系方向与强度研究

doi:10.11959/j.issn.2096-0271.2019027

Abstract

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.

Key words: knowledge graph, direction of relation, strength of relation, negative relation, temporal relation

CLC Number:

TP31

Genlin ZANG, Yaqiang WANG, Qingrong WU, Chunli ZHAN, Xinyang XIE. Study on direction and strength of relation based on knowledge graph[J]. Big Data Research, 2019, 5(3): 96-103.

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References 11

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Study on direction and strength of relation based on knowledge graph

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