mlsanityData to 7 Oct 2026

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

arXiv 2104.00948 ↗First posted 2 Apr 2021cs.IR cs.AI cs.DLcited by 16

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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.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 5 of its 29 references that are in the map. First posted on arXiv 2 Apr 2021.

Builds on

5 of 29 references.

References · 5

  1. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  2. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  3. 2016Automatic Labelling of Topics with Neural Embeddingscited by 12
  4. 2018Using Deep Learning for Title-Based Semantic Subject Indexing to Reach Competitive Performance to Full-Textcited by 10
  5. 2021Ontology-Based Recommendation of Editorial Productscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 4; 2024: 5; 2025: 1; 2026: 3.

16 papers.

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  1. 2022Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge networkcited by 34
  2. 2024Forecasting high-impact research topics via machine learning on evolving knowledge graphscited by 21
  3. 2024Generating Interesting Scientific Ideas using Knowledge Graphs and LLMs: Evaluations with 100 Research Group Leaderscited by 21
  4. 2023We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fieldscited by 20
  5. 2023Construction of Knowledge Graphs: State and Challengescited by 12
  6. 2024Ontology Embedding: A Survey of Methods, Applications and Resourcescited by 12
  7. 2025SemEval-2025 Task 5: LLMs4Subjects -- LLM-based Automated Subject Tagging for a National Technical Library's Open-Access Catalogcited by 10
  8. 2024Efficient Few-shot Learning for Multi-label Classification of Scientific Documents with Many Classescited by 3
  9. 2022Topical Classification of Food Safety Publications with a Knowledge Basecited by 1
  10. 2023SKG: A Versatile Information Retrieval and Analysis Framework for Academic Papers with Semantic Knowledge Graphscited by 1
  11. 2026Knowledge-driven Augmentation and Retrieval for Integrative Temporal Adaptationcited by 1
  12. 2022Characterising Research Areas in the field of AIcited by 0
4 more
  1. 2023Beyond original Research Articles Categorization via NLPcited by 0
  2. 2024Natural Language Querying System Through Entity Enrichmentcited by 0
  3. 2026Research Entity Extraction and Topic Detection from UKRI Grant Proposalscited by 0
  4. 2026COntExt: Towards Context-Aware Ontology Extension from Operational Metricscited by 0