Coarse-to-fine Knowledge Graph Domain Adaptation based on Distantly-supervised Iterative Training-Reference-Cited by-同舟云学术

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Coarse-to-fine Knowledge Graph Domain Adaptation based on Distantly-supervised Iterative Training

Published:2023-12-05 Issue: Volume: Page:1294-1299
ISSN:
Container-title:2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
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Author:

Liao Wenxiong¹,Liu Zhengliang²,Zhang Yiyang¹,Huang Xiaoke¹,Qi Fei¹,Ding Siqi¹,Ren Hui³,Wu Zihao²,Dai Haixing²,Li Sheng⁴,Wu Lingfei⁵,Liu Ninghao²,Li Quanzheng³,Liu Tianming²,Li Xiang³,Cai Hongmin¹

Affiliation:

1. South China University of Technology,GuangZhou,China

2. University of Georgia,Athens,USA

3. Massachusetts General Hospital and Harvard Medical School,Boston,USA

4. University of Technology Virginia,Blacksburg,USA

5. Pinterest Palo,Alto,USA

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Research and Development

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10385250/10385251/10385649.pdf?arnumber=10385649

Reference11 articles.

1. HKGB: An Inclusive, Extensible, Intelligent, Semi-auto-constructed Knowledge Graph Framework for Healthcare with Clinicians’ Expertise Incorporated

2. The Unified Medical Language System

3. Learning a Health Knowledge Graph from Electronic Medical Records

4. T2kg: An end-to-end system for creating knowledge graph from unstructured text;Kertkeidkachorn

5. Seq2KG: An End-to-End Neural Model for Domain Agnostic Knowledge Graph (not Text Graph) Construction from Text

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