梳理45个学术知识组织系统，揭示其异构性与整合需求
A Survey on Knowledge Organization Systems of Research Fields: Resources and Challenges
- 分析45个学术领域知识组织系统，从范围、结构等五维度对比
- 发现系统间在规模、质量、使用上差异巨大，缺乏统一标准
- 适合研究知识管理、学术图谱构建的学者参考
知识组织系统（KOS）如术语表、词表、分类体系和本体，在信息分类、管理与检索中起基础作用。在学术领域，KOS常用于表示研究领域及其关系，旨在对论文、课程、专利、书籍、科研会议、专家、资助项目、软件、实验材料等进行分类。这些结构化表示被众多学科采纳，有效支持人工智能系统实现：提升文献可检索性、量化科研影响力、分析与预测研究动态。本文全面综述学术领域的现有KOS，基于范围、结构、维护、使用及与其他KOS的关联等五个维度，分析比较45个KOS。结果表明，各系统在范围、规模、质量与使用上呈现高度异构性，凸显跨学科知识表示亟需更集成的解决方案。最后讨论主要挑战与未来方向。
原文摘要 · Abstract (English)
Knowledge Organization Systems (KOSs), such as term lists, thesauri, taxonomies, and ontologies, play a fundamental role in categorising, managing, and retrieving information. In the academic domain, KOSs are often adopted for representing research areas and their relationships, primarily aiming to classify research articles, academic courses, patents, books, scientific venues, domain experts, grants, software, experiment materials, and several other relevant products and agents. These structured representations of research areas, widely embraced by many academic fields, have proven effective in empowering AI-based systems to i) enhance retrievability of relevant documents, ii) enable advanced analytic solutions to quantify the impact of academic research, and iii) analyse and forecast research dynamics. This paper aims to present a comprehensive survey of the current KOS for academic disciplines. We analysed and compared 45 KOSs according to five main dimensions: scope, structure, curation, usage, and links to other KOSs. Our results reveal a very heterogeneous scenario in terms of scope, scale, quality, and usage, highlighting the need for more integrated solutions for representing research knowledge across academic fields. We conclude by discussing the main challenges and the most promising future directions.
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