GraphPrompt: Biomedical Entity Normalization Using Graph-based Prompt Templates
Zhang, J.; Wang, Z.; Zhang, S.; Bhalerao, M. M.; Liu, Y.; Zhu, D.; Wang, S.
Show abstract
Biomedical entity normalization unifies the language across biomedical experiments and studies, and further enables us to obtain a holistic view of life sciences. Current approaches mainly study the normalization of more standardized entities such as diseases and drugs, while disregarding the more ambiguous but crucial entities such as pathways, functions and cell types, hindering their real-world applications. To achieve biomedical entity normalization on these under-explored entities, we first introduce an expert-curated dataset OBO-syn encompassing 70 different types of entities and 2 million curated entity-synonym pairs. To utilize the unique graph structure in this dataset, we propose GraphPrompt, a promptbased learning approach that creates prompt templates according to the graphs. Graph-Prompt obtained 41.0% and 29.9% improvement on zero-shot and few-shot settings respectively, indicating the effectiveness of these graph-based prompt templates. We envision that our method GraphPrompt and OBO-syn dataset can be broadly applied to graph-based NLP tasks, and serve as the basis for analyzing diverse and accumulating biomedical data.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- STonKGs: A Sophisticated Transformer Trained on Biomedical Text and Knowledge Graphs 96%
- Neural Collective Matrix Factorization for Integrated Analysis of Heterogeneous Biomedical Data 95%
- BERTMeSH: Deep Contextual Representation Learning for Large-scale High-performance MeSH Indexing with Full Text 94%
Similar papers in this journal
Similar papers in this journal
- DeepGene: An Efficient Foundation Model for Genomics based on Pan-genome Graph Transformer 94%
- Learning universal knowledge graph embedding for predicting biomedical pairwise interactions 92%
- GAT-HiC: Efficient Reconstruction of 3D Chromosome Structure via Residual Graph Attention Neural Networks 91%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.