Results 31 to 40 of about 7,645,086 (295)
Language Model Guided Knowledge Graph Embeddings
IEEE Access, 2022 Knowledge graph embedding models have become a popular approach for knowledge graph completion through predicting the plausibility of (potential) triples.
Mirza Mohtashim Alam +6 more
doaj +1 more source
Efficiently embedding dynamic knowledge graphs
Knowledge-Based Systems, 2022 46 ...
Tianxing Wu 0001 +4 more
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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
core +1 more source
Embedding Knowledge Graph through Triple Base Neural Network and Positive Samples [PDF]
Computer and Knowledge Engineering, 2022 Representation learning on a knowledge graph aims to capture patterns in the knowledge graph as low-dimensional dense distributed representation vectors in the continuous semantic space, which is a powerful technique for predicting missing links in ...
Sogol Haghani, Mohammad Reza Keyvanpour
doaj +1 more source
Knowledge Graph Embedding Model with Entity Description on Cement Manufacturing Domain [PDF]
Jisuanji kexueTo address the problem that many knowledge graph embedding models lack the consideration of semantic information when performing knowledge embedding and cannot extract the semantic information of entities specialized in cement manufactu-ring domain well ...
ZHOU Honglin, SONG Huazhu, ZHANG Juan
doaj +1 more source
Application and evaluation of knowledge graph embeddings in biomedical data [PDF]
PeerJ Computer Science, 2021 Linked data and bio-ontologies enabling knowledge representation, standardization, and dissemination are an integral part of developing biological and biomedical databases.
Mona Alshahrani +2 more
doaj +2 more sources
Binarized Knowledge Graph Embeddings [PDF]
, 2019 Tensor factorization has become an increasingly popular approach to knowledge graph completion(KGC), which is the task of automatically predicting missing facts in a knowledge graph. However, even with a simple model like CANDECOMP/PARAFAC(CP) tensor decomposition, KGC on existing knowledge graphs is impractical in resource-limited environments, as a ...
Koki Kishimoto +4 more
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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
openaire +7 more sources
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
openaire +4 more sources