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

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2021-08
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Attribution-NonCommercial-NoDerivs 3.0 United States
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Ying Ding, Bogdan Arsintescu, Ching-Hua Chen, Haoyun Feng, François Scharffe, Oshani Seneviratne, and Juan Sequeda. 2021. International Workshop on Knowledge Graph: Heterogenous Graph Deep Learning and Applications. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD '21). Association for Computing Machinery, New York, NY, USA, 4121–4122. https://doi.org/10.1145/3447548.3469473
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http://doi.org/10.1145/3447548.3469473; https://hdl.handle.net/20.500.13015/6475
Abstract
Knowledge graph (KG) is the backbone to enable cognitive Artificial Intelligence (AI), which relies on cognitive computing and semantic reasoning. Knowledge graph is 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 our 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.;
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Attribution-NonCommercial-NoDerivs 3.0 United States
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States