@inproceedings{li-etal-2022-ji,
title = "基于中文电子病历知识图谱的实体对齐研究(Research on Entity Alignment Based on Knowledge Graph of {C}hinese Electronic Medical Record)",
author = "Li, Lishuang and
Dong, Jiangyuan",
editor = "Sun, Maosong and
Liu, Yang and
Che, Wanxiang and
Feng, Yang and
Qiu, Xipeng and
Rao, Gaoqi and
Chen, Yubo",
booktitle = "Proceedings of the 21st Chinese National Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Nanchang, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2022.ccl-1.20",
pages = "211--221",
abstract = "{``}医疗知识图谱中知识重叠和互补的现象普遍存在,利用实体对齐进行医疗知识图谱融合成为迫切需要。然而据我们调研,目前医疗领域中的实体对齐尚没有一个完整的处理方案。因此本文提出了一个规范的基于中文电子病历的医疗知识图谱实体对齐流程,为医疗领域的实体对齐提供了一种可行的方案。同时针对基于中文电子病历医疗知识图谱之间结构异构性的特点,设计了一个双视角并行图神经网络丨乄乵乐乎乥乴丩模型用于解决医疗领域实体对齐,并取得较好的效果。{''}",
language = "Chinese",
}
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<abstract>“医疗知识图谱中知识重叠和互补的现象普遍存在,利用实体对齐进行医疗知识图谱融合成为迫切需要。然而据我们调研,目前医疗领域中的实体对齐尚没有一个完整的处理方案。因此本文提出了一个规范的基于中文电子病历的医疗知识图谱实体对齐流程,为医疗领域的实体对齐提供了一种可行的方案。同时针对基于中文电子病历医疗知识图谱之间结构异构性的特点,设计了一个双视角并行图神经网络丨乄乵乐乎乥乴丩模型用于解决医疗领域实体对齐,并取得较好的效果。”</abstract>
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%0 Conference Proceedings
%T 基于中文电子病历知识图谱的实体对齐研究(Research on Entity Alignment Based on Knowledge Graph of Chinese Electronic Medical Record)
%A Li, Lishuang
%A Dong, Jiangyuan
%Y Sun, Maosong
%Y Liu, Yang
%Y Che, Wanxiang
%Y Feng, Yang
%Y Qiu, Xipeng
%Y Rao, Gaoqi
%Y Chen, Yubo
%S Proceedings of the 21st Chinese National Conference on Computational Linguistics
%D 2022
%8 October
%I Chinese Information Processing Society of China
%C Nanchang, China
%G Chinese
%F li-etal-2022-ji
%X “医疗知识图谱中知识重叠和互补的现象普遍存在,利用实体对齐进行医疗知识图谱融合成为迫切需要。然而据我们调研,目前医疗领域中的实体对齐尚没有一个完整的处理方案。因此本文提出了一个规范的基于中文电子病历的医疗知识图谱实体对齐流程,为医疗领域的实体对齐提供了一种可行的方案。同时针对基于中文电子病历医疗知识图谱之间结构异构性的特点,设计了一个双视角并行图神经网络丨乄乵乐乎乥乴丩模型用于解决医疗领域实体对齐,并取得较好的效果。”
%U https://aclanthology.org/2022.ccl-1.20
%P 211-221
Markdown (Informal)
[基于中文电子病历知识图谱的实体对齐研究(Research on Entity Alignment Based on Knowledge Graph of Chinese Electronic Medical Record)](https://aclanthology.org/2022.ccl-1.20) (Li & Dong, CCL 2022)
ACL