Results 11 to 20 of about 8,049,750 (250)
Knowledge Graph Embedding by Dynamic Translation
IEEE Access, 2017 Knowledge graph embedding aims at representing entities and relations in a knowledge graph as dense, low-dimensional and real-valued vectors. It can efficiently measure semantic correlations of entities and relations in knowledge graphs, and improve the ...
Liang Chang +5 more
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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 +2 more sources
On Training Knowledge Graph Embedding Models [PDF]
Information, 2021 Knowledge graph embedding (KGE) models have become popular means for making discoveries in knowledge graphs (e.g., RDF graphs) in an efficient and scalable manner.
Sameh K. Mohamed +2 more
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Structured query construction via knowledge graph embedding [PDF]
, 2022 S.1819-1846In order to facilitate the accesses of general users to knowledge graphs, an increasing effort is being exerted to construct graph-structured queries of given natural language questions.
Decker, S. +4 more
core +2 more sources
Knowledge graph embeddings: open challenges and opportunities [PDF]
, 2023 While Knowledge Graphs (KGs) have long been used as valuable sources of structured knowledge, in recent years, KG embeddings have become a popular way of deriving numeric vector representations from them, for instance, to support knowledge graph ...
Russa Biswas +9 more
core +3 more sources
Knowledge Sheaves: A Sheaf-Theoretic Framework for Knowledge Graph Embedding [PDF]
, 2023 Knowledge graph embedding involves learning representations of entities -- the vertices of the graph -- and relations -- the edges of the graph -- such that the resulting representations encode the known factual information represented by the knowledge ...
Schrater, Paul +2 more
core +1 more source
Learning Graph Embedding With Adversarial Training Methods [PDF]
, 2020 Graph embedding aims to transfer a graph into vectors to facilitate subsequent graph-analytics tasks like link prediction and graph clustering. Most approaches on graph embedding focus on preserving the graph structure or minimizing the reconstruction ...
Jiang, Jing +5 more
core +2 more sources
An Approach to Knowledge Base Completion by a Committee-Based Knowledge Graph Embedding
Applied Sciences, 2020 Knowledge bases such as Freebase, YAGO, DBPedia, and Nell contain a number of facts with various entities and relations. Since they store many facts, they are regarded as core resources for many natural language processing tasks.
Su Jeong Choi +2 more
doaj +1 more source
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 +9 more
core +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