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3D Learning and Reasoning in Link Prediction over Knowledge Graphs

Mojtaba Nayyeri, Mirza Mohtashim Alam, Jens Lehmann, Sahar Vahdati
2020 IEEE Access  
In this work, the mentioned advantages of the Rodrigues rotations are exploited for learning and reasoning over Knowledge Graphs (KGs) with the objective of link prediction for graph completion.  ...  One of the recent domains in which using rotations led to achieving state-of-the-art results is learning and reasoning over Knowledge Graphs (KGs) using embeddings (KGEs).  ... 
doi:10.1109/access.2020.3034183 fatcat:eyftsob7inhitmursy6bjiwg4a

Cascade Graph Neural Networks for RGB-D Salient Object Detection [article]

Ao Luo, Xin Li, Fan Yang, Zhicheng Jiao, Hong Cheng, Siwei Lyu
2020 arXiv   pre-print
Cas-Gnn processes the two data sources individually and employs a novelCascade Graph Reasoning(CGR) module to learn powerful dense feature embeddings, from which the saliency map can be easily inferred  ...  , hindering the performance of RGB-D saliency detectors.In this work, we introduceCascade Graph Neural Networks(Cas-Gnn),a unified framework which is capable of comprehensively distilling and reasoning  ...  Cross-modality Reasoning with Graph Neural Networks We start out with a simple GNN model, which reasons over the cross-modality relations between 2D appearance (color) and 3D geometric (depth) information  ... 
arXiv:2008.03087v1 fatcat:nrjn4fkk65bynf3yyzfjn4wimy

IFR-Explore: Learning Inter-object Functional Relationships in 3D Indoor Scenes [article]

Qi Li, Kaichun Mo, Yanchao Yang, Hang Zhao, Leonidas Guibas
2021 arXiv   pre-print
In this paper, we take the first step in building AI system learning inter-object functional relationships in 3D indoor environments with key technical contributions of modeling prior knowledge by training  ...  Results show that our model successfully learns priors and fast-interactive-adaptation strategies for exploring inter-object functional relationships in complex 3D scenes.  ...  The first stage perceives and reasons over the objects in a novel test scene to predict possible inter-object functional relationships using learned functional priors about the object geometry and scene  ... 
arXiv:2112.05298v2 fatcat:ydkyeiyzefbctpg2t6xdac6phu

It's Not About the Journey; It's About the Destination: Following Soft Paths Under Question-Guidance for Visual Reasoning

Monica Haurilet, Alina Roitberg, Rainer Stiefelhagen
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
Visual Reasoning remains a challenging task, as it has to deal with long-range and multi-step object relationships in the scene.  ...  We present a new model for Visual Reasoning, aimed at capturing the interplay among individual objects in the image represented as a scene graph.  ...  in the graph as input and use fully-connected layers to make a prediction.  ... 
doi:10.1109/cvpr.2019.00203 dblp:conf/cvpr/HauriletRS19 fatcat:62ofenmkd5hq3aopq6zidaugmm

Explainable Link Prediction for Emerging Entities in Knowledge Graphs [article]

Rajarshi Bhowmik, Gerard de Melo
2020 arXiv   pre-print
Our method finds reasoning paths between source and target entities, thereby making the link prediction for unseen entities interpretable and providing support evidence for the inferred link.  ...  Link prediction can alleviate this by inferring a target entity, given a source entity and a query relation.  ...  We also thank Diffbot and Google for providing the computing infrastructure required for this project.  ... 
arXiv:2005.00637v2 fatcat:s2mkqqwbczeadg4qyi2os2ezj4

Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure Evolution [article]

Sankalp Garg, Navodita Sharma, Woojeong Jin, Xiang Ren
2020 arXiv   pre-print
These methods predict the structure of the graph by reasoning over the interactions in the graph at previous time steps.  ...  We jointly train the model link prediction and attribute prediction.  ...  Neural relational inference also looks at the inverse problem of predicting dynamics of graph with attribute information. Temporal Knowledge Graph Reasoning and Link Prediction.  ... 
arXiv:2003.03919v1 fatcat:ma3sqnpysbcqxa3xyslbj5xhly

Report on the First International Workshop on Mining Graphs and Complex Structures (MGCS'07)

Lawrence B. Holder, Xifeng Yan
2008 SIGMOD record  
In "Combining Collective Classification and Link Prediction", Bilgic et al. explore the combination of collective classification and link prediction to see if their iterative application can improve both  ...  The First International Workshop on Mining Graphs and Complex Structures provides researchers a forum on the new development of knowledge discovery in graph and complex data.  ... 
doi:10.1145/1374780.1374795 fatcat:lyebwlg5yfb5rkwkddzqn2hqdq

Special issue on semantic deep learning

Dagmar Gromann, Luis Espinosa Anke, Thierry Declerck, Pascal Hitzler, Krzysztof Janowicz
2019 Semantic Web Journal  
Approaches range from utilizing structured knowledge in the training process of neural networks to enriching such architectures with ontological reasoning mechanisms.  ...  This editorial introduces the Semantic Web Journal special issue on Semantic Deep Learning, which brings together Semantic Web and deep learning research.  ...  This is devoted in par- The edition of this special issue on Semantic Deep Learning has been supported by the German national project DeepLee, which is partially funded by the German Federal Ministry of  ... 
doi:10.3233/sw-190364 fatcat:hnmi2xowhvdvdheagxuzlnmqmu

Reward Prediction Error as an Exploration Objective in Deep RL

Riley Simmons-Edler, Ben Eisner, Daniel Yang, Anthony Bisulco, Eric Mitchell, Sebastian Seung, Daniel Lee
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
In this paper, we distinguish between exploration tasks in which seeking novel states aids in finding new reward, and those where it does not, such as goal-conditioned tasks and escaping local reward maxima  ...  A major challenge in reinforcement learning is exploration, when local dithering methods such as epsilon-greedy sampling are insufficient to solve a given task.  ...  Acknowledgements This research is based upon work supported in part by NSF SMA 18-29268 and United States Office Of Naval Research under Contract No.  ... 
doi:10.24963/ijcai.2020/386 dblp:conf/ijcai/GargSJR20 fatcat:skxk2hcxpbewxfewulsbqkm37a

TemporalGAT: Attention-Based Dynamic Graph Representation Learning [chapter]

Ahmed Fathy, Kan Li
2020 Lecture Notes in Computer Science  
Learning representations for dynamic graphs is fundamental as it supports numerous graph analytic tasks such as dynamic link prediction, node classification, and visualization.  ...  We propose a deep attention model to learn low-dimensional feature representations which preserves the graph structure and features among series of graph snapshots over time.  ...  [27] handle temporal reasoning problem in multi-relational knowledge graphs through employing a recurrent neural network.  ... 
doi:10.1007/978-3-030-47426-3_32 fatcat:njgqbwuujnapxkikqrjpimbp4q

Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs [article]

Woojeong Jin, Meng Qu, Xisen Jin, Xiang Ren
2020 arXiv   pre-print
Knowledge graph reasoning is a critical task in natural language processing. The task becomes more challenging on temporal knowledge graphs, where each fact is associated with a timestamp.  ...  Most existing methods focus on reasoning at past timestamps and they are not able to predict facts happening in the future.  ...  Temporal KG Reasoning and Link Prediction. There are some recent attempts on incorporating temporal information in modeling dynamic knowledge graphs.  ... 
arXiv:1904.05530v4 fatcat:qkftajo335enjgcg5sjigbc3om

Variational Knowledge Graph Reasoning [article]

Wenhu Chen, Wenhan Xiong, Xifeng Yan, William Wang
2018 arXiv   pre-print
Inferring missing links in knowledge graphs (KG) has attracted a lot of attention from the research community.  ...  In order to evaluate our method, we conduct the experiment of the link prediction task on multiple datasets and achieve state-of-the-art performances on both datasets.  ...  This research was sponsored in part by the Army Research Laboratory under cooperative agreements W911NF09-2-0053 and NSF IIS 1528175.  ... 
arXiv:1803.06581v3 fatcat:4mgtajlql5fb3atk2kgqyzd3jq

Spatio-Temporal Graph for Video Captioning with Knowledge Distillation [article]

Boxiao Pan, Haoye Cai, De-An Huang, Kuan-Hui Lee, Adrien Gaidon, Ehsan Adeli, Juan Carlos Niebles
2020 arXiv   pre-print
In this paper, we propose a novel spatio-temporal graph model for video captioning that exploits object interactions in space and time.  ...  Our model builds interpretable links and is able to provide explicit visual grounding.  ...  We thank our anonymous reviewers, Andrey Kurenkov, Chien-Yi Chang, and Ranjay Krishna, for helpful comments and discussion.  ... 
arXiv:2003.13942v1 fatcat:puqoouhvfnguzngzeeaaftdbfy

Increasing information feed in the process of structural steel design

P. Pauwels, T. Jonckheere, R. De Meyer, J. Van Campenhout
2012 International Journal Sustainable Construction & Design  
Thisinvestigation indicates possibilities regarding information reuse in the process of structural steel design and,by extent, in other design contexts as well.  ...  Research initiatives throughout history have shown how a designer typically makes associationsand references to a vast amount of knowledge based on experiences to make decisions.  ...  describe each of their experiences in detail and link them together into one global semantic web graph.  ... 
doi:10.21825/scad.v2i2.20514 fatcat:oygg235rbnfq7l5oohvuymipre

Variational Knowledge Graph Reasoning

Wenhu Chen, Wenhan Xiong, Xifeng Yan, William Yang Wang
2018 Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)  
Inferring missing links in knowledge graphs (KG) has attracted a lot of attention from the research community.  ...  In order to evaluate our method, we conduct the experiment of the link prediction task on multiple datasets and achieve state-of-the-art performances on both datasets.  ...  This research was sponsored in part by the Army Research Laboratory under cooperative agreements W911NF09-2-0053 and NSF IIS 1528175.  ... 
doi:10.18653/v1/n18-1165 dblp:conf/naacl/ChenXYW18 fatcat:rz4vmuzqmvb5llfzp45cxyzkp4
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