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- W4366207356 abstract "The pandemic of COVID-19 has inspired extensive works across different research fields. Existing literature and knowledge platforms on COVID-19 only focus on collecting papers on biology and medicine, neglecting the interdisciplinary efforts, which hurdles knowledge sharing and research collaborations between fields to address the problem. Studying interdisciplinary researches requires effective paper category classification and efficient cross-domain knowledge extraction and integration. In this work, we propose Covidia, COVID-19 interdisciplinary academic knowledge graph to bridge the gap between knowledge of COVID-19 on different domains. We design frameworks based on contrastive learning for disciplinary classification, and propose a new academic knowledge graph scheme for entity extraction, relation classification and ontology management in accordance with interdisciplinary researches. Based on Covidia, we also establish knowledge discovery benchmarks for finding COVID-19 research communities and predicting potential links." @default.
- W4366207356 created "2023-04-19" @default.
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- W4366207356 date "2023-04-14" @default.
- W4366207356 modified "2023-09-27" @default.
- W4366207356 title "Covidia: COVID-19 Interdisciplinary Academic Knowledge Graph" @default.
- W4366207356 doi "https://doi.org/10.48550/arxiv.2304.07242" @default.
- W4366207356 hasPublicationYear "2023" @default.
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