# A Survey on Knowledge Organization Systems of Research Fields: Resources and Challenges

arXiv: https://arxiv.org/abs/2409.04432 · HTML: https://mlsanity.com/paper/2409.04432/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 143 references that are in the map.
- First posted on arXiv 6 Sep 2024.

## Builds on (most cited first, 21 of 21)

- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Reducing hallucination in structured outputs via Retrieval-Augmented Generation](https://mlsanity.com/paper/2404.08189/) (2024)
- [Knowledge Graphs: Opportunities and Challenges](https://mlsanity.com/paper/2303.13948/) (2023)
- [TaxoGen: Unsupervised Topic Taxonomy Construction by Adaptive Term Embedding and Clustering](https://mlsanity.com/paper/1812.09551/) (2018)
- [LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages](https://mlsanity.com/paper/2407.05975/) (2024)
- [Artificial Intelligence for Literature Reviews: Opportunities and Challenges](https://mlsanity.com/paper/2402.08565/) (2024)
- [CoRel: Seed-Guided Topical Taxonomy Construction by Concept Learning and Relation Transferring](https://mlsanity.com/paper/2010.06714/) (2020)
- [Using Deep Learning for Title-Based Semantic Subject Indexing to Reach Competitive Performance to Full-Text](https://mlsanity.com/paper/1801.06717/) (2018)
- [Large Scale Subject Category Classification of Scholarly Papers with Deep Attentive Neural Networks](https://mlsanity.com/paper/2007.13826/) (2020)
- [ConceptScope: Organizing and Visualizing Knowledge in Documents based on Domain Ontology](https://mlsanity.com/paper/2003.05108/) (2020)
- [Key Choices in the Design of Simple Knowledge Organization System (SKOS)](https://mlsanity.com/paper/1302.1224/) (2013)
- [Reducing the Effort for Systematic Reviews in Software Engineering](https://mlsanity.com/paper/1908.06676/) (2019)
- [Ontology-Based Recommendation of Editorial Products](https://mlsanity.com/paper/2103.13526/) (2021)
- [Improving Editorial Workflow and Metadata Quality at Springer Nature](https://mlsanity.com/paper/2103.13527/) (2021)
- [Diversity of Expertise is Key to Scientific Impact: a Large-Scale Analysis in the Field of Computer Science](https://mlsanity.com/paper/2306.15344/) (2023)
- [Knowledge Organization Systems (KOS) in the Semantic Web: A Multi-Dimensional Review](https://mlsanity.com/paper/1801.04479/) (2018)
- [Granularity of algorithmically constructed publication-level classifications of research publications: Identification of specialties](https://mlsanity.com/paper/1901.05273/) (2019)
- [Early Detection of Research Trends](https://mlsanity.com/paper/1912.08928/) (2019)
- [Ontology Extraction and Usage in the Scholarly Knowledge Domain](https://mlsanity.com/paper/2003.12611/) (2020)
- [The association between topic growth and citation impact of research publications](https://mlsanity.com/paper/2106.08708/) (2021)

## Built on by (most cited first, 9 of 9)

- [Large Language Models for Scholarly Ontology Generation: An Extensive Analysis in the Engineering Field](https://mlsanity.com/paper/2412.08258/) (2024)
- [Modelling and Classifying the Components of a Literature Review](https://mlsanity.com/paper/2508.04337/) (2025)
- [Leveraging Large Language Models for Generating Research Topic Ontologies: A Multi-Disciplinary Study](https://mlsanity.com/paper/2508.20693/) (2025)
- [A Socratic RAG Approach to Connect Natural Language Queries on Research Topics with Knowledge Organization Systems](https://mlsanity.com/paper/2502.15005/) (2025)
- [A Comparative Study of Task Adaptation Techniques of Large Language Models for Identifying Sustainable Development Goals](https://mlsanity.com/paper/2506.15208/) (2025)
- [A Hybrid AI Methodology for Generating Ontologies of Research Topics from Scientific Paper Corpora](https://mlsanity.com/paper/2508.04213/) (2025)
- [How Do LLMs Encode Scientific Quality? An Empirical Study Using Monosemantic Features from Sparse Autoencoders](https://mlsanity.com/paper/2602.19115/) (2026)
- [Research Entity Extraction and Topic Detection from UKRI Grant Proposals](https://mlsanity.com/paper/2606.30304/) (2026)
- [Benchmarking Resource-Efficient LLMs for Research Topic Ontology Generation in the Biomedical Field](https://mlsanity.com/paper/2607.17902/) (2026)

## Abstract (arXiv)

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.