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A Web-scale system for scientific knowledge exploration

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arxiv 1805.12216 v1 pith:SKM7E3PF submitted 2018-05-30 cs.CL cs.DL

Zhihong Shen , Hao Ma , Kuansan Wang This is my paper · ORCID
classification cs.CLcs.DL
keywords scientificconceptconceptssystemexplorationhundredsknowledgepublications
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Leveraging Large Language Models for Generating Research Topic Ontologies: A Multi-Disciplinary Study

    cs.DL 2025-08 conditional novelty 6.0 of 10

    Fine-tuned open-weight LLMs classify research-topic relationships with up to 93.5% F1 on a new multi-disciplinary benchmark, and cross-domain transfer loses only about 5 points.

  2. Large Language Models for Scholarly Ontology Generation: An Extensive Analysis in the Engineering Field

    cs.DL 2024-12 conditional novelty 5.0 of 10

    Zero-shot LLMs, especially Claude 3 Sonnet and a fine-tuned 7B Mistral variant, classify semantic relations between engineering research topics with high F1 on the new IEEE-Rel-1K benchmark.

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