# A Web-scale system for scientific knowledge exploration

arXiv: https://arxiv.org/abs/1805.12216 · HTML: https://mlsanity.com/paper/1805.12216/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2018.

## Built on by (most cited first, 30 of 39)

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- [Efficient Few-shot Learning for Multi-label Classification of Scientific Documents with Many Classes](https://mlsanity.com/paper/2410.05770/) (2024)
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- [SciNoBo : A Hierarchical Multi-Label Classifier of Scientific Publications](https://mlsanity.com/paper/2204.00880/) (2022)
- [NLP-KG: A System for Exploratory Search of Scientific Literature in Natural Language Processing](https://mlsanity.com/paper/2406.15294/) (2024)
- [Leveraging Large Language Models for Generating Research Topic Ontologies: A Multi-Disciplinary Study](https://mlsanity.com/paper/2508.20693/) (2025)
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- [Hierarchical Classification of Research Fields in the "Web of Science" Using Deep Learning](https://mlsanity.com/paper/2302.00390/) (2023)

## Abstract (arXiv)

To enable efficient exploration of Web-scale scientific knowledge, it is necessary to organize scientific publications into a hierarchical concept structure. In this work, we present a large-scale system to (1) identify hundreds of thousands of scientific concepts, (2) tag these identified concepts to hundreds of millions of scientific publications by leveraging both text and graph structure, and (3) build a six-level concept hierarchy with a subsumption-based model. The system builds the most comprehensive cross-domain scientific concept ontology published to date, with more than 200 thousand concepts and over one million relationships.