📄A Review of Knowledge Graph Completion
📘Journal: Information (I.F=3.38)
🗓 publish year: 2022
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📲Channel: @ComplexNetworkAnalysis
#paper #Review #Knowledge_Graph
📘Journal: Information (I.F=3.38)
🗓 publish year: 2022
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Review #Knowledge_Graph
👍3
📄From social networks to knowledge graphs: A plea for interdisciplinary approaches
📘Journal: Social Sciences and Humanities Open
🗓Publish year: 2022
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📱Channel: @ComplexNetworkAnalysis
#paper #knowledge_graph #plea #interdisciplinary #approaches
📘Journal: Social Sciences and Humanities Open
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #knowledge_graph #plea #interdisciplinary #approaches
📄A Survey on Knowledge Graph Embeddings for Link Prediction
📘Journal: SYMMETRY-BASEL (I.F=2.94)
🗓Publish year: 2021
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📱Channel: @ComplexNetworkAnalysis
#paper #knowledge_graph #Embeddings #Link_Prediction #Survey
📘Journal: SYMMETRY-BASEL (I.F=2.94)
🗓Publish year: 2021
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📱Channel: @ComplexNetworkAnalysis
#paper #knowledge_graph #Embeddings #Link_Prediction #Survey
👍1
🎞 Machine Learning with Graphs: Heterogeneous & Knowledge Graph Embedding, Knowledge Graph Completion
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥 In this lecture, we first introduce the heterogeneous graph with the definition and several examples. In the next, we talk about a model called RGCN which extends the GCN to heterogeneous graph. To make the model more scalable, several approximated approaches are introduced, including block diagonal matrices and basis learning. At last, we show how RGCN predicts the labels of nodes and links.
Then we introduce the knowledge graphs by giving several examples and applications.
📽 Watch: part1 part2
📝 slide
💻 Code
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Knowledge_Graph #GNN
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥 In this lecture, we first introduce the heterogeneous graph with the definition and several examples. In the next, we talk about a model called RGCN which extends the GCN to heterogeneous graph. To make the model more scalable, several approximated approaches are introduced, including block diagonal matrices and basis learning. At last, we show how RGCN predicts the labels of nodes and links.
Then we introduce the knowledge graphs by giving several examples and applications.
📽 Watch: part1 part2
📝 slide
💻 Code
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Knowledge_Graph #GNN
YouTube
Stanford CS224W: ML with Graphs | 2021 | Lecture 10.1-Heterogeneous & Knowledge Graph Embedding
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3pNkBLE
Lecture 10.1 - Heterogeneous Graphs and Knowledge Graph Embeddings
Jure Leskovec
Computer Science, PhD
In this lecture,…
Lecture 10.1 - Heterogeneous Graphs and Knowledge Graph Embeddings
Jure Leskovec
Computer Science, PhD
In this lecture,…
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📄Construction of Knowledge Graphs: Current State and Challenges
🗓Publish year: 2023
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📲Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graph
🗓Publish year: 2023
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📲Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graph
👍2
🎞 Machine Learning with Graphs: Reasoning in Knowledge Graphs, Answering Predictive Queries, Query2box: Reasoning over KGs
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥 IIn this lecture, we introduce how to perform reasoning over knowledge graphs and provide answers to complex queries. We talk about different possible queries that one can get over a knowledge graph, and how to answer them by traversing over the graph. We also show how incompleteness of knowledge graphs can limit our ability to provide complete answers. We finally talk about how we can solve this problem by generalizing the link prediction task.
📽 Watch: part1 part2 part3
📝 slide
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Knowledge_Graph
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥 IIn this lecture, we introduce how to perform reasoning over knowledge graphs and provide answers to complex queries. We talk about different possible queries that one can get over a knowledge graph, and how to answer them by traversing over the graph. We also show how incompleteness of knowledge graphs can limit our ability to provide complete answers. We finally talk about how we can solve this problem by generalizing the link prediction task.
📽 Watch: part1 part2 part3
📝 slide
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Knowledge_Graph
YouTube
Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 11.1 - Reasoning in Knowledge Graphs
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3BweHQZ
Lecture 11.1 - Reasoning in Knowledge Graphs using Embeddings
Jure Leskovec
Computer Science, PhD
In this lecture, we introduce…
Lecture 11.1 - Reasoning in Knowledge Graphs using Embeddings
Jure Leskovec
Computer Science, PhD
In this lecture, we introduce…
👍1
📄Construction of Knowledge Graphs: Current State and Challenges
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graph #Current_State
#Challenges
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graph #Current_State
#Challenges
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Forwarded from Bioinformatics
📄 A comprehensive review on knowledge graphs for complex diseases
📘 Journal: Briefings in Bioinformatics (I.F.=9.5)
🗓 Publish year: 2023
🧑💻Authors: Yang Yang, Yuwei Lu, Wenying Yan
🏢University: Soochow University, Suzhou, China
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📲Channel: @Bioinformatics
#review #knowledge_graph #diease
📘 Journal: Briefings in Bioinformatics (I.F.=9.5)
🗓 Publish year: 2023
🧑💻Authors: Yang Yang, Yuwei Lu, Wenying Yan
🏢University: Soochow University, Suzhou, China
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📲Channel: @Bioinformatics
#review #knowledge_graph #diease
👍2
📃 A systematic literature review of knowledge graph construction and application in education
📘 Journal: Heliyon (I.F=4)
🗓 Publish year: 2023
🧑💻Authors: Bilal Abu-Salih , Salihah Alotaibi
🏢Universities: The University of Jordan, Imam Mohammad Ibn Saud Islamic University (IMSIU),
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📲Channel: @ComplexNetworkAnalysis
#review #knowledge_graph #education #review
📘 Journal: Heliyon (I.F=4)
🗓 Publish year: 2023
🧑💻Authors: Bilal Abu-Salih , Salihah Alotaibi
🏢Universities: The University of Jordan, Imam Mohammad Ibn Saud Islamic University (IMSIU),
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📲Channel: @ComplexNetworkAnalysis
#review #knowledge_graph #education #review
👍5
📃 Drug-drug interactions prediction based on deep learning and knowledge graph: a review
📘 Journal: iScience (I.F=6.107)
🗓 Publish year: 2024
🧑💻Authors: Huimin Luo, Weijie Yin, Jianlin Wang, Wenjuan Liang, Junwei Luo, Chaokun Yan
🏢University: Henan University, Kaifeng, China, Henan Polytechnic University, Jiaozuo, China
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📱Channel: @ComplexNetworkAnalysis
#paper #Drug #prediction #Deep_learning #knowledge_graph #review
📘 Journal: iScience (I.F=6.107)
🗓 Publish year: 2024
🧑💻Authors: Huimin Luo, Weijie Yin, Jianlin Wang, Wenjuan Liang, Junwei Luo, Chaokun Yan
🏢University: Henan University, Kaifeng, China, Henan Polytechnic University, Jiaozuo, China
📎 Study the paper
📱Channel: @ComplexNetworkAnalysis
#paper #Drug #prediction #Deep_learning #knowledge_graph #review
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