Abstract
There has been rapid growth in biomedical literature, yet capturing the heterogeneity of the bibliographic information of these articles remains relatively understudied. Graph neural networks have gained popularity, however, they may not fully capture the information available in the PubMed database, a biomedical literature repository containing over 33 million articles. We introduce PubMed Graph Benchmark (PGB), a new benchmark dataset for evaluating heterogeneous graph representations. PGB is one of the largest heterogeneous networks to date and aggregates the rich metadata into a unified source including abstract, authors, citations, keywords, and the associated keyword hierarchy. The benchmark contains an evaluation task of 21 systematic review topics, an essential knowledge translation tool.
| Original language | English |
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| Title of host publication | CIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management |
| Publisher | Association for Computing Machinery |
| Pages | 5331-5335 |
| Number of pages | 5 |
| ISBN (Electronic) | 9798400701245 |
| DOIs | |
| State | Published - Oct 21 2023 |
| Event | 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023 - Birmingham, United Kingdom Duration: Oct 21 2023 → Oct 25 2023 |
Publication series
| Name | International Conference on Information and Knowledge Management, Proceedings |
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Conference
| Conference | 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023 |
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| Country/Territory | United Kingdom |
| City | Birmingham |
| Period | 10/21/23 → 10/25/23 |
Funding
Acknowledgements. We thank the reviewers for their insightful suggestions and comments. This work was supported by the National Science Foundation award IIS-2145411.
Keywords
- Heterogeneous Information Network
- Network Embedding
- PubMed Benchmark
- Systematic Review