GraphPrompt: Graph-Based Prompt Templates for Biomedical Synonym Prediction Article Swipe
Jiayou Zhang
,
Zhirui Wang
,
Shizhuo Zhang
,
Megh Manoj Bhalerao
,
Yucong Liu
,
Dawei Zhu
,
Sheng Wang
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2112.03002
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2112.03002
In the expansion of biomedical dataset, the same category may be labeled with different terms, thus being tedious and onerous to curate these terms. Therefore, automatically mapping synonymous terms onto the ontologies is desirable, which we name as biomedical synonym prediction task. Unlike biomedical concept normalization (BCN), no clues from context can be used to enhance synonym prediction, making it essential to extract graph features from ontology. We introduce an expert-curated dataset OBO-syn encompassing 70 different types of concepts and 2 million curated concept-term pairs for evaluating synonym prediction methods. We find BCN methods perform weakly on this task for not making full use of graph information. Therefore, we propose GraphPrompt, a prompt-based learning approach that creates prompt templates according to the graphs. GraphPrompt obtained 37.2\% and 28.5\% 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. All the data and codes are avalible at: https://github.com/HanwenXuTHU/GraphPrompt
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2112.03002
- https://arxiv.org/pdf/2112.03002
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4308536748
All OpenAlex metadata
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https://openalex.org/W4308536748Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2112.03002Digital Object Identifier
- Title
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GraphPrompt: Graph-Based Prompt Templates for Biomedical Synonym PredictionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-11-13Full publication date if available
- Authors
-
Jiayou Zhang, Zhirui Wang, Shizhuo Zhang, Megh Manoj Bhalerao, Yucong Liu, Dawei Zhu, Sheng WangList of authors in order
- Landing page
-
https://arxiv.org/abs/2112.03002Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2112.03002Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2112.03002Direct OA link when available
- Concepts
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Synonym (taxonomy), Computer science, Graph, Template, Information retrieval, Natural language processing, Artificial intelligence, Task (project management), Semantic similarity, Theoretical computer science, Programming language, Botany, Genus, Biology, Management, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.concepts | 78 |
| abstract_inverted_index.dataset, | 5 |
| abstract_inverted_index.envision | 144 |
| abstract_inverted_index.features | 64 |
| abstract_inverted_index.few-shot | 132 |
| abstract_inverted_index.learning | 113 |
| abstract_inverted_index.methods. | 89 |
| abstract_inverted_index.obtained | 124 |
| abstract_inverted_index.settings | 133 |
| abstract_inverted_index.according | 119 |
| abstract_inverted_index.analyzing | 166 |
| abstract_inverted_index.different | 13, 75 |
| abstract_inverted_index.essential | 60 |
| abstract_inverted_index.expansion | 2 |
| abstract_inverted_index.introduce | 68 |
| abstract_inverted_index.ontology. | 66 |
| abstract_inverted_index.templates | 118 |
| abstract_inverted_index.zero-shot | 130 |
| abstract_inverted_index.Therefore, | 24, 107 |
| abstract_inverted_index.biomedical | 4, 38, 43, 170 |
| abstract_inverted_index.desirable, | 33 |
| abstract_inverted_index.evaluating | 86 |
| abstract_inverted_index.indicating | 135 |
| abstract_inverted_index.ontologies | 31 |
| abstract_inverted_index.prediction | 40, 88 |
| abstract_inverted_index.synonymous | 27 |
| abstract_inverted_index.templates. | 142 |
| abstract_inverted_index.GraphPrompt | 123, 148 |
| abstract_inverted_index.graph-based | 140, 157 |
| abstract_inverted_index.improvement | 128 |
| abstract_inverted_index.prediction, | 57 |
| abstract_inverted_index.GraphPrompt, | 110 |
| abstract_inverted_index.accumulating | 169 |
| abstract_inverted_index.concept-term | 83 |
| abstract_inverted_index.encompassing | 73 |
| abstract_inverted_index.information. | 106 |
| abstract_inverted_index.prompt-based | 112 |
| abstract_inverted_index.automatically | 25 |
| abstract_inverted_index.effectiveness | 137 |
| abstract_inverted_index.normalization | 45 |
| abstract_inverted_index.respectively, | 134 |
| abstract_inverted_index.expert-curated | 70 |
| abstract_inverted_index.https://github.com/HanwenXuTHU/GraphPrompt | 180 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/12 |
| sustainable_development_goals[0].score | 0.5099999904632568 |
| sustainable_development_goals[0].display_name | Responsible consumption and production |
| citation_normalized_percentile |