研究领域知识组织系统综述：资源与挑战
A Survey on Knowledge Organization Systems of Research Fields: Resources and Challenges
AI总结：
本文综述并从范围、结构、策展、使用和互链五维度比较45个学术领域KOS，揭示其异构性，并指出跨领域研究知识组织的集成需求与未来挑战。
摘要：
知识组织系统（Knowledge Organization Systems，KOSs），例如术语表、叙词表、分类法和本体，在信息的分类、管理和检索中发挥着基础性作用。在学术领域，KOSs 常被用于表示研究领域及其关系，主要目标是对研究论文、学术课程、专利、书籍、科学出版物/会议场所、领域专家、资助项目、软件、实验材料以及若干其他相关产物和主体进行分类。这些研究领域的结构化表示已被许多学术领域广泛采用，并被证明能够有效支持基于 AI 的系统：i）提升相关文献的可检索性，ii）支持用于量化学术研究影响力的高级分析方案，以及 iii）分析和预测研究动态。本文旨在对当前面向学术学科的 KOS 进行全面综述。我们按照五个主要维度分析并比较了 45 个 KOS：范围、结构、策展、使用以及与其他 KOS 的链接。结果揭示了在范围、规模、质量和使用方面高度异构的局面，凸显出需要更集成的解决方案来跨学术领域表示研究知识。最后，我们讨论了主要挑战和最有前景的未来方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
