PubMed knowledge graph 2.0: Connecting papers, patents, and clinical trials in biomedical science
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Abstract (arXiv)
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.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Oct 2024.
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Citing papers per year: 2025: 3; 2026: 5.
8 papers.
- 2025Automated Construction of Medical Indicator Knowledge Graphs Using Retrieval Augmented Large Language Modelscited by 2
- 2025DeepEvidence: Empowering Biomedical Discovery with Deep Knowledge Graph Researchcited by 2
- 2025Developing Large Language Models for Clinical Research Using One Million Clinical Trialscited by 1
- 2026Building evidence-based knowledge bases from full-text literature for disease-specific biomedical reasoningcited by 1
- 2026LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrievalcited by 0
- 2026From Regulatory Approvals to Patents: Cross-Domain Linking for Cardiovascular Device Traceabilitycited by 0
- 2026ANCHOR-RE: An Agentic Neuro-Symbolic Framework for Grounded Biomedical Relation Extractioncited by 0
- 2026PriDyG: Privacy-preserving Dynamic Graph Inference with LLM-GNN Collaborationcited by 0
- 2026PriDyG: Privacy-preserving Dynamic Graph Inference with LLM-GNN Collaboration4 Aug 2026
- 2026ANCHOR-RE: An Agentic Neuro-Symbolic Framework for Grounded Biomedical Relation Extraction4 Aug 2026
- 2026From Regulatory Approvals to Patents: Cross-Domain Linking for Cardiovascular Device Traceability6 Jun 2026
- 2026LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrieval20 Apr 2026
- 2026Building evidence-based knowledge bases from full-text literature for disease-specific biomedical reasoning30 Mar 2026
- 2025DeepEvidence: Empowering Biomedical Discovery with Deep Knowledge Graph Research23 Dec 2025
- 2025Automated Construction of Medical Indicator Knowledge Graphs Using Retrieval Augmented Large Language Models17 Nov 2025
- 2025Developing Large Language Models for Clinical Research Using One Million Clinical Trials22 May 2025