Results 41 to 50 of about 244,866 (345)
SMR: Medical Knowledge Graph Embedding for Safe Medicine Recommendation
, 2021 Most of the existing medicine recommendation systems that are mainly based on electronic medical records (EMRs) are significantly assisting doctors to make better clinical decisions benefiting both patients and caregivers.
Gong, F +4 more
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What Is Learned in Knowledge Graph Embeddings? [PDF]
, 2022 A knowledge graph (KG) is a data structure which represents entities and relations as the vertices and edges of a directed graph with edge types. KGs are an important primitive in modern machine learning and artificial intelligence. Embedding-based models, such as the seminal TransE [Bordes et al., 2013] and the recent PairRE [Chao et al., 2020] are ...
Michael R. Douglas +6 more
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Knowledge Graph Embedding by Translating on Hyperplanes
AAAI Conference on Artificial Intelligence, 2014 We deal with embedding a large scale knowledge graph composed of entities and relations into a continuous vector space. TransE is a promising method proposed recently, which is very efficient while achieving state-of-the-art predictive performance ...
Zhen Wang +3 more
semanticscholar +1 more source
Knowledge Graph Embeddings [PDF]
, 2012 With the growing popularity of multi-relational data on the Web, knowledge graphs (KGs) have become a key data source in various application domains, such as Web search, question answering, and natural language understanding. In a typical KG such as Freebase (Bollacker et al.
Rosso, Paolo +2 more
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Hypernetwork Knowledge Graph Embeddings [PDF]
, 2019 Knowledge graphs are graphical representations of large databases of facts, which typically suffer from incompleteness. Inferring missing relations (links) between entities (nodes) is the task of link prediction. A recent state-of-the-art approach to link prediction, ConvE, implements a convolutional neural network to extract features from concatenated
Ivana Balazevic +2 more
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Analogical Inference Enhanced Knowledge Graph Embedding [PDF]
AAAI Conference on Artificial Intelligence, 2023 Knowledge graph embedding (KGE), which maps entities and relations in a knowledge graph into continuous vector spaces, has achieved great success in predicting missing links in knowledge graphs.
Zhen Yao +5 more
semanticscholar +1 more source
Knowledge Graph Embedding Technology: A Review
Jisuanji kexue yu tansuo, 2021 Knowledge graph embedding (KGE) is a new research hotspot in the field of knowledge graphs, which aims to apply the translation invariance of word vectors to embedding entities and relationships of the knowledge graph into a low-dimensional vector space ...
SHU Shitai, LI Song+, HAO Xiaohong, ZHANG Liping
doaj +1 more source
Holographic Embeddings of Knowledge Graphs
Proceedings of the AAAI Conference on Artificial Intelligence, 2016 Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs. In this work, we propose holographic embeddings (HolE) to learn compositional vector space representations of entire knowledge graphs.
Maximilian Nickel +2 more
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Knowledge Graph Embedding Compression [PDF]
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020 Knowledge graph (KG) representation learning techniques that learn continuous embeddings of entities and relations in the KG have become popular in many AI applications. With a large KG, the embeddings consume a large amount of storage and memory. This is problematic and prohibits the deployment of these techniques in many real world settings. Thus, we
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Knowledge Association with Hyperbolic Knowledge Graph Embeddings [PDF]
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020 EMNLP ...
Zequn Sun 0001 +5 more
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