Results 51 to 60 of about 7,092,434 (358)
Survey on Inductive Learning for Knowledge Graph Completion [PDF]
Knowledge graph completion can make knowledge graph more complete. However, traditional knowledge graph completion methods assume that all test entities and relations appear in the training process.
LIANG Xinyu, SI Guannan, LI Jianxin, TIAN Pengxin, AN Zhaoliang, ZHOU Fengyu
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Building a knowledge graph to enable precision medicine
Measurement(s) knowledge graph • Relation Code • textual entity Technology Type(s) machine learning • computational modeling technique Developing personalized diagnostic strategies and targeted treatments requires a deep understanding of disease biology ...
P. Chandak, Kexin Huang, M. Zitnik
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Deep Bidirectional Language-Knowledge Graph Pretraining [PDF]
Pretraining a language model (LM) on text has been shown to help various downstream NLP tasks. Recent works show that a knowledge graph (KG) can complement text data, offering structured background knowledge that provides a useful scaffold for reasoning.
Michihiro Yasunaga+6 more
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<p>This survey discusses the concept of knowledge graphs, including their construction, extraction, and applications. Various tools such as Zotero, Web of Science, Google Scholar, EndNote, and VosViewer are used to analyze and visualize collected data. A Boolean query mechanism ensures the gathered material is relevant to the study.
Abiola Akinnubi, Jeremiah Ajiboye
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Knowledge Questions from Knowledge Graphs [PDF]
We address the novel problem of automatically generating quiz-style knowledge questions from a knowledge graph such as DBpedia. Questions of this kind have ample applications, for instance, to educate users about or to evaluate their knowledge in a specific domain. To solve the problem, we propose an end-to-end approach.
Seyler, D., Yahya, M., Berberich, K.
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Building a PubMed knowledge graph [PDF]
AbstractPubMed® is an essential resource for the medical domain, but useful concepts are either difficult to extract or are ambiguous, which has significantly hindered knowledge discovery. To address this issue, we constructed a PubMed knowledge graph (PKG) by extracting bio-entities from 29 million PubMed abstracts, disambiguating author names ...
Xu, Jian+14 more
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Objectives: The subtype classification of lung adenocarcinoma is important for treatment decision. This study aimed to investigate the deep learning and radiomics networks for predicting histologic subtype classification and survival of lung ...
Chengdi Wang+9 more
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RAGAT: Relation Aware Graph Attention Network for Knowledge Graph Completion
Knowledge graph completion (KGC) is the task of predicting missing links based on known triples for knowledge graphs. Several recent works suggest that Graph Neural Networks (GNN) that exploit graph structures achieve promising performance on KGC.
Xiyang Liu+3 more
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Low-Dimensional Hyperbolic Knowledge Graph Embeddings [PDF]
Knowledge graph (KG) embeddings learn low- dimensional representations of entities and relations to predict missing facts. KGs often exhibit hierarchical and logical patterns which must be preserved in the embedding space.
Ines Chami+5 more
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Knowledge Graphs of Kawasaki Disease [PDF]
AbstractKawasaki Disease is a vasculitis syndrome that is extremely harmful to children. Kawasaki Disease can cause severe symptoms of ischemic heart disease or develop into ischemic heart disease, leading to death in children. Researchers and clinicians need to analyze various knowledge and data resources to explore aspects of Kawasaki Disease ...
Guanghua Liu+7 more
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