GSSL Robustness to Real-World Noise in Biomedical Graphs
A new study evaluates Graph Self-Supervised Learning (GSSL) methods on text-driven biomedical knowledge graphs, addressing a gap in robustness research that previously focused on synthetic perturbations. The authors introduce NATD-GSSL, a framework combining automatic graph construction, refinement, and GSSL. Using a dual-graph protocol, they compare a noisy graph from MedMentions with a clean UMLS reference graph for unsupervised term typing. The work highlights challenges posed by real-world noise in automatically extracted graphs.
Key facts
- First comprehensive evaluation of GSSL methods on text-driven graphs for unsupervised term typing.
- Introduces NATD-GSSL framework.
- Uses dual-graph protocol contrasting noisy MedMentions graph with clean UMLS reference graph.
- Addresses real-world noise, not synthetic perturbations.
- Focuses on biomedical domain.
- Published on arXiv with ID 2605.05463.
- Leverages NLP for automatic knowledge graph extraction.
- Graph Self-Supervised Learning paradigm used.
Entities
Institutions
- arXiv
- MedMentions
- Unified Medical Language System (UMLS)