图书情报工作

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2024 , Vol. 68 >Issue 23: 4 - 16

DOI: https://doi.org/10.13266/j.issn.0252-3116.2024.23.001

专题：领域知识组织理论与实践

领域知识结构认知——基于大数据环境的适用性分析

  • 杨欣谊 ,
  • 苏新宁
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  • 1 南京大学信息管理学院 南京 210023;
    2 数据工程与知识服务省高校重点实验室(南京大学) 南京 210023;
    3 南京大学国家安全发展研究院 南京 210023
杨欣谊,博士研究生。

收稿日期: 2024-03-29

  修回日期: 2024-08-14

  网络出版日期: 2024-12-04

基金资助

本文系国家社会科学基金重点项目"大数据环境下领域知识加工与组织模式研究"(项目编号:20ATQ006)和江苏省研究生科研与实践创新计划项目"基于异质信息网络的领域知识结构演化分析"(项目编号:KYCX23_0077)研究成果之一。

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Domain Knowledge Structure Cognition: An Applicability Analysis in Big Data Environments

  • Yang Xinyi ,
  • Su Xinning
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  • 1 School of Information Management, Nanjing University, Nanjing 210023;
    2 Key Laboratory of Data Engineering and Knowledge Services in Provincial Universities (Nanjing University), Nanjing 210023;
    3 National Security Development Research Institute of Nanjing University, Nanjing 210023

Received date: 2024-03-29

  Revised date: 2024-08-14

  Online published: 2024-12-04

Supported by

This work is supported by the National Social Science Fund of China project titled “Research on Processing and Organization Pattern of Domain Knowledge in Big Data Environment”(Grant No.20ATQ006),and by the Graduate Research and Innovation of Jiangsu Province project title “Evolutionary Analysis of Domain Knowledge Structure based on Heterogeneous Information Network”(Grant No.KYCX23_0077).

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摘要

[目的/意义] 大数据环境下，知识泛在、碎片化、规模庞大且动态变化，认知和把握已有领域知识结构，能为科学高效的领域知识组织提供借鉴。[方法/过程] 领域知识结构认知剖析知识单元关联形成的结构，体现组合形成的领域知识的原理、内涵及框架。从不同视角、环境、应用目的组织的知识呈现出不同的知识结构。《中国图书馆分类法》和《汉语主题词表》架构了知识单元之间的等级、等同和相关关系，前者侧重知识类别归属建构了树状跨领域的知识体系，后者通过概念组配、相关关系和多重隶属关联连接主题知识形成网状结构，表述主题关联拓展的知识。语义关联知识结构通过三元组连接形成语义网络，表述类别归属、属性和语义关系的知识，并由推理表述隐含知识。[结果/结论] 大数据环境下，知识单元之间建构了多重隶属、交叉连接的树状等级结构，表述泛在知识的类别归属；主题法实现了碎片化、细粒度知识的粗粒化、概念化表述，同时语义关系更加明确具体；语义关联的知识结构更加动态、灵活，能够提供基于推理的知识；领域知识结构向适用于大数据环境知识组织的方向演进。

关键词： 领域知识组织; 结构认知; 分类体系; 主题关联; 语义知识组织

本文引用格式

杨欣谊 , 苏新宁 . 领域知识结构认知——基于大数据环境的适用性分析[J]. 图书情报工作, 2024 , 68(23) : 4 -16 . DOI: 10.13266/j.issn.0252-3116.2024.23.001

Abstract

[Purpose/Significance] In big data environments, knowledge is ubiquitous, fragmented and large in scale.Recognizing and grasping the existing domain knowledge structures can provide a reference for scientific and efficient domain knowledge organization.[Method/Process] The cognition of domain knowledge structure helped explore the relationships between knowledge units.It showed the principles, connotations, and frameworks of domain knowledge formed by the combinations.Knowledge organized from different perspectives, contexts and applications could present different knowledge structures.The Chinese Library Classification and Chinese Thesaurus has provided hierarchical, equivalent, and related relationships between knowledge units.The former one focused on knowledge category construction to build a tree-like knowledge system, while the latter connected thematic knowledge through concept coordination, related relationships, and multiple memberships, forming a semantic network to express topic related and extended knowledge.The semantic knowledge associative structure constructed the triples to form a semantic network.These networks expressed knowledge related to category affiliation, attributes, and semantic relationships.And they represented implicit knowledge through axiomatic reasoning.[Result/Conclusion] In the big data environment, a tree-like hierarchical structure with multiple affiliations and cross-connections is constructed to represent the category attribution of ubiquitous knowledge.The thematic approach achieves the coarse-grained and conceptual representation of fragmented and fine-grained knowledge, while the semantic relations are more explicit and specific.Semantic associative knowledge structures are more flexible and extensible, which can provide reasoning knowledge.The domain knowledge structures are evolving towards being suitable for knowledge organization in big data environments.

Key words： domain knowledge organization; structure cognition; classification system; topic association; semantic knowledge organization

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