# Building a PubMed knowledge graph

arXiv: https://arxiv.org/abs/2005.04308 · HTML: https://mlsanity.com/paper/2005.04308/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2020.

## Built on by (most cited first, 30 of 38)

- [Graph Condensation via Receptive Field Distribution Matching](https://mlsanity.com/paper/2206.13697/) (2022)
- [BERN2: an advanced neural biomedical named entity recognition and normalization tool](https://mlsanity.com/paper/2201.02080/) (2022)
- [Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective](https://mlsanity.com/paper/2411.14258/) (2024)
- [Accelerating science with human-aware artificial intelligence](https://mlsanity.com/paper/2306.01495/) (2023)
- [BERT Based Clinical Knowledge Extraction for Biomedical Knowledge Graph Construction and Analysis](https://mlsanity.com/paper/2304.10996/) (2023)
- [A Semantic Web Technology Index](https://mlsanity.com/paper/2201.07034/) (2022)
- [A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises](https://mlsanity.com/paper/2306.04802/) (2023)
- [Explainable AI for Bioinformatics: Methods, Tools, and Applications](https://mlsanity.com/paper/2212.13261/) (2022)
- [Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models](https://mlsanity.com/paper/2305.12544/) (2023)
- [Biomedical Knowledge Graph: A Survey of Domains, Tasks, and Real-World Applications](https://mlsanity.com/paper/2501.11632/) (2025)
- [Enabling Scalable Evaluation of Bias Patterns in Medical LLMs](https://mlsanity.com/paper/2410.14763/) (2024)
- [Question Answering Over Biological Knowledge Graph via Amazon Alexa](https://mlsanity.com/paper/2210.06040/) (2022)
- [Representation Learning for Person or Entity-centric Knowledge Graphs: An Application in Healthcare](https://mlsanity.com/paper/2305.05640/) (2023)
- [From Large Language Models to Knowledge Graphs for Biomarker Discovery in Cancer](https://mlsanity.com/paper/2310.08365/) (2023)
- [Accelerating science with human versus alien artificial intelligences](https://mlsanity.com/paper/2104.05188/) (2021)
- [A Biomedical Knowledge Graph for Biomarker Discovery in Cancer](https://mlsanity.com/paper/2302.04737/) (2023)
- [Hierarchical Tree-structured Knowledge Graph For Academic Insight Survey](https://mlsanity.com/paper/2402.04854/) (2024)
- [Augmenting Biomedical Named Entity Recognition with General-domain Resources](https://mlsanity.com/paper/2406.10671/) (2024)
- [ExKG-LLM: Leveraging Large Language Models for Automated Expansion of Cognitive Neuroscience Knowledge Graphs](https://mlsanity.com/paper/2503.06479/) (2025)
- [MultiHal: Multilingual Dataset for Knowledge-Graph Grounded Evaluation of LLM Hallucinations](https://mlsanity.com/paper/2505.14101/) (2025)
- [Understanding the Effect of Knowledge Graph Extraction Error on Downstream Graph Analyses: A Case Study on Affiliation Graphs](https://mlsanity.com/paper/2506.12367/) (2025)
- [KnowGraph@IITK at SemEval-2021 Task 11: Building KnowledgeGraph for NLP Research](https://mlsanity.com/paper/2104.01619/) (2021)
- [Complementary artificial intelligence designed to augment human discovery](https://mlsanity.com/paper/2207.00902/) (2022)
- [Predicting the clinical citation count of biomedical papers using multilayer perceptron neural network](https://mlsanity.com/paper/2210.06346/) (2022)
- [Fish-bone diagram of research issue: Gain a bird's-eye view on a specific research topic](https://mlsanity.com/paper/2407.01553/) (2024)
- [A Systematic Evaluation of Knowledge Graph Embeddings for Gene-Disease Association Prediction](https://mlsanity.com/paper/2504.08445/) (2025)
- [Developing Large Language Models for Clinical Research Using One Million Clinical Trials](https://mlsanity.com/paper/2505.16097/) (2025)
- [The Effect of Gender Diversity on Scientific Team Impact: A Team Roles Perspective](https://mlsanity.com/paper/2512.23429/) (2025)
- [Building evidence-based knowledge bases from full-text literature for disease-specific biomedical reasoning](https://mlsanity.com/paper/2603.28325/) (2026)
- [Unifying biomedical knowledge in a modern multimodal graph](https://mlsanity.com/paper/2604.27269/) (2026)

## Abstract (arXiv)

PubMed is an essential resource for the medical domain, but useful concepts are either difficult to extract or are ambiguated, which has significantly hindered knowledge discovery. To address this issue, we constructed a PubMed knowledge graph (PKG) by extracting bio-entities from 29 million PubMed abstracts, disambiguating author names, integrating funding data through the National Institutes of Health (NIH) ExPORTER, collecting affiliation history and educational background of authors from ORCID, and identifying fine-grained affiliation data from MapAffil. Through the integration of the credible multi-source data, we could create connections among the bio-entities, authors, articles, affiliations, and funding. Data validation revealed that the BioBERT deep learning method of bio-entity extraction significantly outperformed the state-of-the-art models based on the F1 score (by 0.51%), with the author name disambiguation (AND) achieving a F1 score of 98.09%. PKG can trigger broader innovations, not only enabling us to measure scholarly impact, knowledge usage, and knowledge transfer, but also assisting us in profiling authors and organizations based on their connections with bio-entities. The PKG is freely available on Figshare (https://figshare.com/s/6327a55355fc2c99f3a2, simplified version that exclude PubMed raw data) and TACC website (http://er.tacc.utexas.edu/datasets/ped, full version).