摘要
【目的/意义】大数据互联网时代,知识以碎片化形式分散在大数据环境中,加剧了大知识融合的难度,深刻地影响了知识的组织和创新过程。如何针对大数据环境下碎片化知识的特征,构建出适于系统性的把握知识和解决复杂问题的知识融合框架就成为大数据知识工程研究的热点问题【方法/过程】系统性分析了碎片化知识的特征,在此基础上构建了基于知识超网络的融合框架,探讨了知识融合的标准、知识融合的维度以及知识融合的机制。【结果/结论】知识超网络模型是碎片化知识非线性融合的一种可行的研究框架,其面向问题的多维、多级、多层的综合的知识融合模型为大数据环境下碎片化知识的融合提供了新的思路。
[Purpose/significance]In the era of big data Internet,knowledge is dispersed in the environment of big data in a fragmented form,which aggravates the difficulty of large knowledge fusion,and profoundly affects the organization and innovation process of knowledge.How to construct a knowledge fusion framework which is suitable for systematic grasping knowledge and solving complex problems for the characteristics of fragmented knowledge in big data environment has become a hot issue in big data knowledge engineering research.[Method/process]This paper systematically analyzes the characteristics of fragmented knowledge,constructs a fusion framework based on knowledge supemetwork,and discusses the criteria,dimensions and mechanism of knowledge fusion.[Result/conclusion]The knowledge supernetwork model is a feasible research framework for the nonlinear fusion of fragmented knowledge,and its problem-oriented multi-dimensional,multi-level and comprehensive knowledge fusion model provides a new idea for the fusion of fragmented knowledge in the context of big data.
作者
高国伟
段佳琪
GAO Guo-wei;DUAN Jia-qi(Liaoning Normal University,Dalian 116029,China)
出处
《情报科学》
CSSCI
北大核心
2020年第1期17-23,共7页
Information Science
基金
辽宁省社科规划基金项目“基于知识超网络的碎片化知识非线性融合方法研究”(L19BTQ002)
关键词
知识超网络
碎片化知识
知识融合模型
knowledge supernetwork
fragmented knowledge
Knowledge Fusion Model
