领域知识组织理论基础及方法分类简述

A Brief Introduction to the Theoretical Basis and Method Classification of Domain Knowledge Organization

[目的/意义]互联网环境下人们之所以能够快速便捷地从网络中获取自己需要的信息和知识,是因为所能检索到的信息和知识来源于组织有序的系统,而知识组织则是形成有序系统的关键。文章通过对知识组织方法的梳理和分类,明确了知识组织的核心任务以及其在情报工作乃至知识检索领域的重要作用,以期使得知识组织在大数据时代中仍旧能够得到情报工作人员的重视,得以继续发展。[方法/过程]梳理知识组织发展的脉络,依据功能的不同将知识组织方法分为基础知识架构类知识组织方法、分类体系构建类知识组织方法和关系网络建立类知识组织方法,叙述这些知识组织方法的理论基础,并阐释每类方法在大数据领域知识组织环境下的作用和功能。[结论/结果]为了在新环境下最大化地发挥其效用,领域知识组织的发展可以做出逻辑表达语言专业化、更加个性化并注重知识的实时更新、自动逻辑推理和人机交互,更加智能化等方面的调整,以便能够更好地迎接大数据时代的挑战。

[Purpose/significance]The reason why people can obtain the information and knowledge which they need quickly and conveniently in the Internet environment is that the information and knowledge they can retrieve come from an organized system,and knowledge organization is the key to forming an organized system.By sorting and classifying knowledge organization methods,this paper clarifies the core tasks of knowledge organization and its important role in information work and even in the field of knowledge retrieval.So that knowledge organization can still receive the attention of information workers and continue to develop in the newly arrived era of big data.[Method/process]We sort out the development of knowledge organization,classify knowledge organization methods into basic knowledge architecture organization method,classification system construction organization method and relationship network establishment organization method according to their functions.Then we describe the theoretical foundations of these knowledge organization methods,and explain the roles and functions of each method in the knowledge organization environment of big data.[Conclusion/result]In order to maximize its utility in the new environment,the development of domain knowledge organization can make adjustments in terms of specialization of logical expression language,more personalization and focus on real-time updating of knowledge,automatic logical reasoning and human-computer interaction,and more intelligence,so as to be able to better meet the challenges of the big data era.