International Workshop on Knowledge Graph: Heterogenous Graph Deep Learning and Applications

被引：0

作者：

Ding, Ying ^{[1
]}

Arsintescu, Bogdan ^{[2
]}

Chen, Ching-Hua ^{[3
]}

Feng, Haoyun ^{[4
]}

Scharffe, Francois ^{[5
]}

Seneviratne, Oshani ^{[6
]}

Sequeda, Juan ^{[7
]}

机构：

[1] Univ Texas Austin, Austin, TX 78712 USA

[2] LinkedIN, Sunnyvale, CA USA

[3] IBM Corp, Armonk, NY USA

[4] Anthem, Indianapolis, IN USA

[5] Columbia Univ, New York, NY 10027 USA

[6] RPI, Troy, NY USA

[7] Dataworld, Austin, TX USA

来源：

KDD '21: PROCEEDINGS OF THE 27TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING | 2021年

关键词：

Knowledge Graph; Graph Mining; Artificial Intelligence;

D O I：

10.1145/3447548.3469473

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Knowledge graph (KG) is the backbone to enable cognitive Artificial Intelligence (AI), which relies on cognitive computing and semantic reasoning. Knowledge graphs the connected data with the semantically enriched context. It is the necessary step for the next move of AI. Our daily activities have closely intermingled with various applications powered by knowledge graphs. It has even entered out healthcare system to facilitate clinical decision making and improve hospital efficiency. This workshop aims to bring researchers and practitioners to promote research and applications related to knowledge graph.

引用

页码：4121 / 4122

页数：2

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