# The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles

arXiv: https://arxiv.org/abs/2104.00948 · HTML: https://mlsanity.com/paper/2104.00948/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 2 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Automatic Labelling of Topics with Neural Embeddings](https://mlsanity.com/paper/1612.05340/) (2016)
- [Using Deep Learning for Title-Based Semantic Subject Indexing to Reach Competitive Performance to Full-Text](https://mlsanity.com/paper/1801.06717/) (2018)
- [Ontology-Based Recommendation of Editorial Products](https://mlsanity.com/paper/2103.13526/) (2021)

## Built on by (most cited first, 16 of 16)

- [Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network](https://mlsanity.com/paper/2210.00881/) (2022)
- [Forecasting high-impact research topics via machine learning on evolving knowledge graphs](https://mlsanity.com/paper/2402.08640/) (2024)
- [Generating Interesting Scientific Ideas using Knowledge Graphs and LLMs: Evaluations with 100 Research Group Leaders](https://mlsanity.com/paper/2405.17044/) (2024)
- [We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fields](https://mlsanity.com/paper/2310.14870/) (2023)
- [Construction of Knowledge Graphs: State and Challenges](https://mlsanity.com/paper/2302.11509/) (2023)
- [Ontology Embedding: A Survey of Methods, Applications and Resources](https://mlsanity.com/paper/2406.10964/) (2024)
- [SemEval-2025 Task 5: LLMs4Subjects -- LLM-based Automated Subject Tagging for a National Technical Library's Open-Access Catalog](https://mlsanity.com/paper/2504.07199/) (2025)
- [Efficient Few-shot Learning for Multi-label Classification of Scientific Documents with Many Classes](https://mlsanity.com/paper/2410.05770/) (2024)
- [Topical Classification of Food Safety Publications with a Knowledge Base](https://mlsanity.com/paper/2201.00374/) (2022)
- [SKG: A Versatile Information Retrieval and Analysis Framework for Academic Papers with Semantic Knowledge Graphs](https://mlsanity.com/paper/2306.04758/) (2023)
- [Knowledge-driven Augmentation and Retrieval for Integrative Temporal Adaptation](https://mlsanity.com/paper/2604.22098/) (2026)
- [Characterising Research Areas in the field of AI](https://mlsanity.com/paper/2205.13471/) (2022)
- [Beyond original Research Articles Categorization via NLP](https://mlsanity.com/paper/2309.07020/) (2023)
- [Natural Language Querying System Through Entity Enrichment](https://mlsanity.com/paper/2410.15753/) (2024)
- [Research Entity Extraction and Topic Detection from UKRI Grant Proposals](https://mlsanity.com/paper/2606.30304/) (2026)
- [COntExt: Towards Context-Aware Ontology Extension from Operational Metrics](https://mlsanity.com/paper/2607.29553/) (2026)

## Abstract (arXiv)

Classifying research papers according to their research topics is an important task to improve their retrievability, assist the creation of smart analytics, and support a variety of approaches for analysing and making sense of the research environment. In this paper, we present the CSO Classifier, a new unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), a comprehensive ontology of re-search areas in the field of Computer Science. The CSO Classifier takes as input the metadata associated with a research paper (title, abstract, keywords) and returns a selection of research concepts drawn from the ontology. The approach was evaluated on a gold standard of manually annotated articles yielding a significant improvement over alternative methods.