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Amazon’s open-source tools make embedding knowledge graphs much more efficient - Amazon Science

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Knowledge graphs are data structures consisting of entities (the nodes of the graph) and relationships between them (the edges, usually depicted as line segments connecting nodes). The entities “Nile River” and “Africa”, for instance, might be connected in a knowledge graph by the relationship “is in”. At Amazon, we use knowledge graphs to represent relationships between products and to encode information for Alexa’s question-answering service, among other things. Recently, we also built a knowledge graph that represented medical and biological information to help find treatments for COVID-19. Today, many applications of knowledge graphs involve knowledge graph embedding, or representing the entities and relations in a knowledge graph as points in a vector space. To make knowledge graph embeddings easier to use, our team has released a set of tools called DGL-KE, for deep-graph-learning knowledge embeddings. Last week at SIGIR, the Association for Computing Machinery’s annual conferenc

Information and knowledge management Amazon’s open-source tools make embedding knowledge graphs much more efficient Tools include optimizations for multicore, multiple-GPU, and distributed-training settings. By Da Zheng August 6, 2020 5 min read Share Share Copy link Email X LinkedIn Facebook Line Reddit QZone Sina Weibo WeChat WhatsApp 分享到微信 x Conference SIGIR 2020 Related publications DGL-KE: Training knowledge graph embeddings at scale Knowledge graphs are data structures consisting of entities (the nodes of the graph) and relationships between them (the edges , usually depicted as line seg

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