PubMed knowledge graph 2.0: Connecting papers, patents, and clinical trials in biomedical science

Papers, patents, and clinical trials are essential scientific resources in biomedicine, crucial for knowledge sharing and dissemination. However, these documents are often stored in disparate databases with varying management standards and data formats, making it challenging to form systematic and fine-grained connections among them. To address this issue, we construct PKG 2.0, a comprehensive knowledge graph dataset encompassing over 36 million papers, 1.3 million patents, and 0.48 million clinical trials in the biomedical field. PKG 2.0 integrates these dispersed resources through 482 million biomedical entity linkages, 19 million citation linkages, and 7 million project linkages. The construction of PKG 2.0 wove together fine-grained biomedical entity extraction, high-performance author name disambiguation, multi-source citation integration, and high-quality project data from the NIH Exporter. Data validation demonstrates that PKG 2.0 excels in key tasks such as author disambiguation and biomedical entity recognition. This dataset provides valuable resources for biomedical researchers, bibliometric scholars, and those engaged in literature mining.

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Publication Details

Journal
Scientific Data
Published
2025-06-17
DOI
https://doi.org/10.1038/s41597-025-05343-8
Citations
10
Primary Topic
Biomedical Text Mining and Ontologies
Type
article
Field-Weighted Citation Impact
8.06

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article

PubMed knowledge graph 2.0: Connecting papers, patents, and clinical trials in biomedical science

Yi Bu, Mujeen Sung, Chao Yu, Jian Xu et al.
10 citations
Scientific Data
Biomedical Text Mining and Ontologies
8.06
article

PubMed knowledge graph 2.0: Connecting papers, patents, and clinical trials in biomedical science

Yi Bu, Mujeen Sung, Chao Yu, Jian Xu, Min Song, Ying Ding, Jaewoo Kang, Vetle I. Torvik, Jiawei Xu
article en
10 citations

Abstract

Papers, patents, and clinical trials are essential scientific resources in biomedicine, crucial for knowledge sharing and dissemination. However, these documents are often stored in disparate databases with varying management standards and data formats, making it challenging to form systematic and fine-grained connections among them. To address this issue, we construct PKG 2.0, a comprehensive knowledge graph dataset encompassing over 36 million papers, 1.3 million patents, and 0.48 million clinical trials in the biomedical field. PKG 2.0 integrates these dispersed resources through 482 million biomedical entity linkages, 19 million citation linkages, and 7 million project linkages. The construction of PKG 2.0 wove together fine-grained biomedical entity extraction, high-performance author name disambiguation, multi-source citation integration, and high-quality project data from the NIH Exporter. Data validation demonstrates that PKG 2.0 excels in key tasks such as author disambiguation and biomedical entity recognition. This dataset provides valuable resources for biomedical researchers, bibliometric scholars, and those engaged in literature mining.

Scientific DataVol. 12(1)
University of Illinois Urbana-Champaign (US), Sun Yat-sen University (CN), Yonsei University (KR), Korea University (KR), Peking University (CN), Kyung Hee University (KR), The University of Texas at Austin (US)
National Natural Science Foundation of China, Natural Science Foundation of Guangdong Province
Openalex Percentile: Top 2%
Biomedical Text Mining and Ontologies
8.06
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