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Knowledge graph using resource description framework and connectionist theory

Ravi Lourdusamy, Xavierlal J Mattam
<span title="">2020</span> <i title="IOP Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wxgp7pobnrfetfizidmpebi4qy" style="color: black;">Journal of Physics, Conference Series</a> </i> &nbsp;
The weighted RDF in Graph Neural Network will represent the knowledge graph using RDF and connectionist theory.  ...  This article presents the use of weighted RDF as a vector embedding of RDF that could be used with Bayesian networks in Graph Neural Networks.  ...  Estimating the importance of a node enhances the utility of the Knowledge Graphs and makes it useful in many applications.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1088/1742-6596/1427/1/012001">doi:10.1088/1742-6596/1427/1/012001</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uqnd3tliczdhtarlu256inzjtu">fatcat:uqnd3tliczdhtarlu256inzjtu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200103022851/https://iopscience.iop.org/article/10.1088/1742-6596/1427/1/012001/pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/1e/4e/1e4e6576e4cb4e8826e4a6e3f707026de314e49b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1088/1742-6596/1427/1/012001"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> iop.org </button> </a>

AutoNet: Knowledge Graphs for Occasions Object Recognition

Zheng LIU, Yang HUANG
<span title="2018-08-31">2018</span> <i title="DEStech Publications"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/52spf3of4jbu5da2xrhvubclcy" style="color: black;">DEStech Transactions on Computer Science and Engineering</a> </i> &nbsp;
Object recognition in images fields is a novel and importance challenge in Computer Vision.  ...  We utilize end-to-end learning on graphs, which is introducing the Gated Graph Neural Network (GGNN) and the Gated Graph Choose Search Neural Network (GG-CSNN) as a way of efficiently incorporating large  ...  Gated Graph Neural Networks In Graph Neural Network, there is no point in initializing entity node representations for input in the graph neural network, because of the contraction map constraint ensures  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.12783/dtcse/ccnt2018/24744">doi:10.12783/dtcse/ccnt2018/24744</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/et7b6q7ovbadzn2yyk7q4eeyuq">fatcat:et7b6q7ovbadzn2yyk7q4eeyuq</a> </span>
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Binarized Graph Neural Network [article]

Hanchen Wang, Defu Lian, Ying Zhang, Lu Qin, Xiangjian He, Yiguang Lin, Xuemin Lin
<span title="2020-04-19">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This motivates us to develop a binarized graph neural network to learn the binary representations of the nodes with binary network parameters following the GNN-based paradigm.  ...  Recently, there have been some breakthroughs in graph analysis by applying the graph neural networks (GNNs) following a neighborhood aggregation scheme, which demonstrate outstanding performance in many  ...  As to our best knowledge, there is no existing work on the binarized graph embedding based on GNNs. Binarized Neural Networks Binarized neural networks was first proposed by BNN [3] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.11147v1">arXiv:2004.11147v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qdaw22pbwjbgdk3kxjpnsak7ea">fatcat:qdaw22pbwjbgdk3kxjpnsak7ea</a> </span>
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The More You Know: Using Knowledge Graphs for Image Classification [article]

Kenneth Marino, Ruslan Salakhutdinov, Abhinav Gupta
<span title="2017-04-22">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This paper investigates the use of structured prior knowledge in the form of knowledge graphs and shows that using this knowledge improves performance on image classification.  ...  We build on recent work on end-to-end learning on graphs, introducing the Graph Search Neural Network as a way of efficiently incorporating large knowledge graphs into a vision classification pipeline.  ...  This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1612.04844v2">arXiv:1612.04844v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/r536v42zfbccpfkkptpzolcpdq">fatcat:r536v42zfbccpfkkptpzolcpdq</a> </span>
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Transferable Graph Neural Networks for Inferring Road Type Attributes in Street Networks

Chidubem Iddianozie, Gavin McArdle
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
In this paper, we study transferable graph neural networks for street networks.  ...  The use of Graph Neural Networks in a transfer learning setting is a promising approach to overcome issues such as the lack of good quality data for training purposes.  ...  Hence, we seek to handle this phenomenon in our framework by sampling important nodes used for training the neural networks.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3128839">doi:10.1109/access.2021.3128839</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/j4aqottacnf3xglfxrtmxgsa7m">fatcat:j4aqottacnf3xglfxrtmxgsa7m</a> </span>
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Distilling Holistic Knowledge with Graph Neural Networks [article]

Sheng Zhou, Yucheng Wang, Defang Chen, Jiawei Chen, Xin Wang, Can Wang, Jiajun Bu
<span title="2021-08-12">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The holistic knowledge is represented as a unified graph-based embedding by aggregating individual knowledge from relational neighborhood samples with graph neural networks, the student network is learned  ...  In this paper, we propose to distill the novel holistic knowledge based on an attributed graph constructed among instances.  ...  Inspired by recent success of graph neural networks in simultaneously modeling the network topology and node attributes, we utilize graph neural networks to extract holistic knowledge from the teacher  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.05507v1">arXiv:2108.05507v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zauj4nf6kjfndoqh6eaukmh6qq">fatcat:zauj4nf6kjfndoqh6eaukmh6qq</a> </span>
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Relation Matters in Sampling: A Scalable Multi-Relational Graph Neural Network for Drug-Drug Interaction Prediction [article]

Arthur Feeney and Rishabh Gupta and Veronika Thost and Rico Angell and Gayathri Chandu and Yash Adhikari and Tengfei Ma
<span title="2021-05-28">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Sampling is an established technique to scale graph neural networks to large graphs.  ...  We propose an approach to modeling the importance of relation types for neighborhood sampling in graph neural networks and show that we can learn the right balance: relation-type probabilities that reflect  ...  proposed a neural network to learn the optimum probability for importance sampling of the nodes in each layer.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2105.13975v1">arXiv:2105.13975v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xms3hru3fvetxgon6rhurm2ls4">fatcat:xms3hru3fvetxgon6rhurm2ls4</a> </span>
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The Use of Graph Databases for Artificial Neural Networks

Doğa Barış ÖZDEMİR, Ahmet Cumhur KINACI
<span title="2021-03-20">2021</span> <i title="Journal of Advanced Research in Natural and Applied Sciences"> Journal of Advanced Research in Natural and Applied Sciences </i> &nbsp;
An artificial neural network can be structurally expressed as a graph. Therefore, it would be much more useful to store ANN models in a database and use the graph database as this database system.  ...  Article History Abstract − Storing and using trained artificial neural network (ANN) models face technical difficulties. These models are usually stored as files and cannot be run directly.  ...  Ahmet Cumhur KINACI: Designed and analyzed the artificial neural network processes on graphs approach. Conflicts of Interest The authors declare no conflict of interest.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.28979/jarnas.890552">doi:10.28979/jarnas.890552</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qbpxorsr3zb3zb6vhbccdsgcvu">fatcat:qbpxorsr3zb3zb6vhbccdsgcvu</a> </span>
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Learning Graph Neural Networks with Positive and Unlabeled Nodes [article]

Man Wu, Shirui Pan, Lan Du, Xingquan Zhu
<span title="2021-03-08">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Graph neural networks (GNNs) are important tools for transductive learning tasks, such as node classification in graphs, due to their expressive power in capturing complex interdependency between nodes  ...  In this paper, we propose a novel graph neural network framework, long-short distance aggregation networks (LSDAN), to overcome these limitations.  ...  Baselines To the best of our knowledge, there is no existing study on positive unlabeled graph neural network learning.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2103.04683v1">arXiv:2103.04683v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7fofa7qkd5ggfdusspij5axwvu">fatcat:7fofa7qkd5ggfdusspij5axwvu</a> </span>
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OcclusionFusion: Occlusion-aware Motion Estimation for Real-time Dynamic 3D Reconstruction [article]

Wenbin Lin, Chengwei Zheng, Jun-Hai Yong, Feng Xu
<span title="2022-03-15">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In our technique, the motion of visible regions is first estimated and combined with temporal information to infer the motion of the occluded regions through an LSTM-involved graph neural network.  ...  Furthermore, our method computes the confidence of the estimated motion by modeling the network output with a probabilistic model, which alleviates untrustworthy motions and enables robust tracking.  ...  The graph neural network uses both the motion of the visible nodes and the complete node graph as input.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2203.07977v1">arXiv:2203.07977v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kvthn6oj5bcudp5kkx77x4xaya">fatcat:kvthn6oj5bcudp5kkx77x4xaya</a> </span>
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Predicting Learners Need for Recommendation Using Dynamic Graph-Based Knowledge Tracing [chapter]

Abdessamad Chanaa, Nour-Eddine El Faddouli
<span title="">2020</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
Based on learners individual knowledge level, we propose a new model that can predict learners needs for recommendation using dynamic graph-based knowledge tracing.  ...  Personalized recommendation as a practical approach to overcoming information overloading has been widely used in e-learning.  ...  Previous work tries to predict student proficiency by modelling knowledge concepts into nodes using a deep graph neural network [9] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-030-52240-7_9">doi:10.1007/978-3-030-52240-7_9</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ht6cnr2abfcgvacznd6vh3xbke">fatcat:ht6cnr2abfcgvacznd6vh3xbke</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200912001026/https://link.springer.com/content/pdf/10.1007%2F978-3-030-52240-7_9.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/6d/58/6d58664a5307fac0f95d7f971d64749ee91525ad.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-030-52240-7_9"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Knowledge Graph Completion: A Review

Zhe Chen, Yuehan Wang, Bin Zhao, Jing Cheng, Xin Zhao, Zongtao Duan
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
in knowledge graph and mining unknown facts.  ...  Knowledge graph completion (KGC) is a hot topic in knowledge graph construction and related applications, which aims to complete the structure of knowledge graph by predicting the missing entities or relationships  ...  ACKNOWLEDGMENT This work was supported in part by the Funds for Key Research and Development Plan Project of the Shaanxi Province, China, under Grant 2017GY-072, 2019ZDLGY17-08, 2020ZDLGY09-02.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.3030076">doi:10.1109/access.2020.3030076</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jbimdngcmrbx3jhihsgrp62cxq">fatcat:jbimdngcmrbx3jhihsgrp62cxq</a> </span>
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Hepatocellular Carcinoma Intra-arterial Treatment Response Prediction for Improved Therapeutic Decision-Making [article]

Junlin Yang, Nicha C. Dvornek, Fan Zhang, Julius Chapiro, MingDe Lin, Aaron Abajian, James S. Duncan
<span title="2019-12-01">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Our graph neural network model seamlessly combines heterogeneous inputs of baseline MR scans, pre-treatment clinical information, and planned treatment characteristics and has been validated on patients  ...  In addition, the pipeline incorporates uncertainty estimation to select hard cases and most align with the misclassified cases.  ...  GCN is the graph convolutional neural network model in the above proposed pipeline.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1912.00411v1">arXiv:1912.00411v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dz5mrykopraszhxpsk5cdb6lai">fatcat:dz5mrykopraszhxpsk5cdb6lai</a> </span>
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Estimating the State of Epidemics Spreading with Graph Neural Networks [article]

Abhishek Tomy, Matteo Razzanelli, Francesco Di Lauro, Daniela Rus, Cosimo Della Santina
<span title="2021-05-10">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We analyze the capability of deep neural networks to solve this challenging task. Our proposed architecture is based on Graph Convolutional Neural Networks.  ...  As such it can reason on the effect of the underlying social network structure, which is recognized as the main component in the spreading of an epidemic.  ...  These operations are graphically summarized in the left part of Fig. 3 . Neural architecture Graph Neural Networks operate in the domain of the graph. In the graph, each node comes with its label.  ... 
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Reinforcement learning on graphs: A survey [article]

Nie Mingshuo, Chen Dongming, Wang Dongqi
<span title="2022-04-27">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Furthermore, we propose important directions and challenges to be solved in the future.  ...  Graph mining tasks arise from many different application domains, ranging from social networks, transportation to E-commerce, etc., which have been receiving great attention from the theoretical and algorithmic  ...  There is a limited number of scholars working on the explainability for graph neural networks, which is important for improving the performance of graph neural network models and assisting in understanding  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2204.06127v2">arXiv:2204.06127v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7wf6qxnxzza7xbiwjgjmrsrdjq">fatcat:7wf6qxnxzza7xbiwjgjmrsrdjq</a> </span>
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