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基于多源异构数据挖掘的“红色记忆”知识图谱构建 被引量:12

Construction of“Red Memory”Knowledge Graph Based on Multi-source Heterogeneous Data Mining
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摘要 [目的/意义]中华民族与中国共产党人对真理追求的过程形成红色文化资源,对其进行知识组织和挖掘构建"红色记忆",不仅能够提升民族自信与凝聚力,更是坚定文化自信的重要途径。针对红色文化资源所存在的分布广、来源多、类型杂、内容有限、组织程度低等问题,构建基于多源异构数据挖掘的"红色记忆"知识图谱,以充分利用红色文化资源。[方法/过程]首先通过设计概念、关系及属性构建红色文化资源本体库,完成"红色记忆"的知识建模工作;其次通过多渠道采集红色文化资源,具体分析红色文化资源的构成和特点,针对这些多源异构数据进行实体、属性、关系识别采取;最后通过图数据库存储构建"红色记忆"知识图谱。[结果/结论]通过构建"红色记忆"知识图谱,能够对多源异构的红色文化资源数据进行深层关系挖掘,提升红色文化资源的组织水平,为实现红色文化智能化服务奠定基础。 [Purpose/significance]Red cultural resources are produced in the process of the Chinese nation and the Chinese Communists’pursuit of truth.Constructing“red memory”by organizing and mining knowledge of red cultural resources can not only enhance national self-confidence and cohesiveness,but also be an important part of cultural self-confidence.There may be many problems when using red cultural resources,such as wide distribution,multiple sources and types,limited content and low degree of organization.In order to make full use of red cultural resources,this paper constructs a“red memory”knowledge graph based on multi-source heterogeneous data.[Method/process]Firstly,this paper constructed a red cultural resource ontology library for knowledge modeling of“red memory”.Secondly,it analyzed the composition and characteristics of red cultural resources collected through multiple channels and extract entities,attributes,relationships.Finally,the“red memory”knowledge graph was constructed through knowledge fusion and storage.[Result/conclusion]By constructing the“red memory”knowledge graph,it is possible to mine deep relationship on multi-source heterogeneous red cultural resource data,improve the organization degree of red cultural resources,and realize of intelligent services of red cultural resources.
作者 郭嘉欣 Guo Jiaxin(Information Management School of Central China Normal University,Wuhan 430079)
出处 《知识管理论坛》 2020年第1期59-68,共10页 Knowledge Management Forum
关键词 红色文化资源 知识图谱构建 知识建模 red cultural resources knowledge graph construction knowledge modeling
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