Filters








65,839 Hits in 4.7 sec

Learning to Efficiently Propagate for Reasoning on Knowledge Graphs [article]

Zhaocheng Zhu, Xinyu Yuan, Louis-Pascal Xhonneux, Ming Zhang, Maxime Gazeau, Jian Tang
2022 arXiv   pre-print
In this paper, we propose A*Net, an efficient model for path-based reasoning on knowledge graphs.  ...  Path-based methods are more appealing solutions than embedding methods for knowledge graph reasoning, due to their interpretability and generalization ability to unseen graphs.  ...  Conclusion In this work, we propose A*Net to efficiently learn path-based methods for knowledge graph reasoning.  ... 
arXiv:2206.04798v1 fatcat:ymkhcojpknarrcw2trgxoa5tj4

Learning to Propagate Knowledge in Web Ontologies

Pasquale Minervini, Claudia d'Amato, Nicola Fanizzi, Volker Tresp
2014 International Semantic Web Conference  
The increasing availability of structured machine-processable knowledge in the WEB OF DATA calls for machine learning methods to support standard pattern matching and reasoning based services (such as  ...  similarities between individuals, and efficiently propagating knowledge across their relations.  ...  As in graph-based semi-supervised learning (SSL) methods [5] , we rely on a similarity graph among examples for propagating knowledge.  ... 
dblp:conf/semweb/MinervinidFT14 fatcat:w2gei5i5kzfkri5wd76kinfjmi

The More You Know: Using Knowledge Graphs for Image Classification [article]

Kenneth Marino, Ruslan Salakhutdinov, Abhinav Gupta
2017 arXiv   pre-print
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.  ...  One characteristic that sets humans apart from modern learning-based computer vision algorithms is the ability to acquire knowledge about the world and use that knowledge to reason about the visual world  ...  The US Government is authorized to reproduce and distribute the reprints for governmental purposed notwithstanding any copyright annotation therein.  ... 
arXiv:1612.04844v2 fatcat:r536v42zfbccpfkkptpzolcpdq

Possibilistic Graphical Models [chapter]

C. Borgelt, J. Gebhardt, R. Kruse
2000 Computational Intelligence in Data Mining  
In this paper we provide an overview on the state of the art of possibilistic networks w.r.t. to propagation and learning algorithms.  ...  Graphical modeling is an important method to efficiently represent and analyze uncertain information in knowledge-based systems.  ...  Management Systems) and in a cooperation with Deutsche Aerospace for the design of a data fusion tool [2] .  ... 
doi:10.1007/978-3-7091-2588-5_3 fatcat:qklssnkhj5hgnh4y7c364tcrqq

Subject index, volume 17, 1997

1997 International Journal of Approximate Reasoning  
bounds propagation, Bayesian networks extension to probability intervals, 17:37 Evolutionary process, three-stage, learning descriptive and approximate fuzzy- logic-controller knowledge bases, 17  ...  , efficient, Bayesian networks extension to probability intervals, 17:37 Cascaded genetic algorithm, fuzzy-system design improvement, 17:351 Chain graph, recovery algorithm, 17:265 Conditional independence  ... 
doi:10.1016/s0888-613x(97)00061-3 fatcat:76zysbtsfjfb3mhqs2sfia5pvu

Learning High-order Structural and Attribute information by Knowledge Graph Attention Networks for Enhancing Knowledge Graph Embedding [article]

Wenqiang Liu, Hongyun Cai, Xu Cheng, Sifa Xie, Yipeng Yu, Hanyu Zhang
2019 arXiv   pre-print
The goal of representation learning of knowledge graph is to encode both entities and relations into a low-dimensional embedding spaces.  ...  Many recent works have demonstrated the benefits of knowledge graph embedding on knowledge graph completion task, such as relation extraction.  ...  the goal but have not been explored much for knowledge graph embedding, we propose Knowledge Graph Attention Networks for Enhancing Knowledge Graph Embedding (KANE).  ... 
arXiv:1910.03891v2 fatcat:xwojhphmubhr7ntmn6zvj6im6a

Multi-Label Zero-Shot Learning with Structured Knowledge Graphs [article]

Chung-Wei Lee, Wei Fang, Chih-Kuan Yeh, Yu-Chiang Frank Wang
2018 arXiv   pre-print
With such investigation of structured knowledge graphs for visual reasoning, we show that our model can be applied for solving multi-label classification and ML-ZSL tasks.  ...  In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which is able to predict multiple unseen class labels for each input instance.  ...  More recently, [30] extended neural networks for graphs [40, 27] to efficiently learn a model that reasons about different types of relationships between class labels by propagating information in  ... 
arXiv:1711.06526v2 fatcat:jssexvmbwzhctou7jy5n7jo24i

Multi-label Zero-Shot Learning with Structured Knowledge Graphs

Chung-Wei Lee, Wei Fang, Chih-Kuan Yeh, Yu-Chiang Frank Wang
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
With such investigation of structured knowledge graphs for visual reasoning, we show that our model can be applied for solving multi-label classification and ML-ZSL tasks.  ...  In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which is able to predict multiple unseen class labels for each input instance.  ...  More recently, [30] extended neural networks for graphs [40, 27] to efficiently learn a model that reasons about different types of relationships between class labels by propagating information in  ... 
doi:10.1109/cvpr.2018.00170 dblp:conf/cvpr/LeeFYW18 fatcat:nspq2q7e7bhnnf3svks4yi4cpi

A comprehensive survey of entity alignment for knowledge graphs

Kaisheng Zeng, Chengjiang Li, Lei Hou, Juanzi Li, Ling Feng
2021 AI Open  
A B S T R A C T Knowledge Graphs (KGs), as a structured human knowledge, manage data in an ease-of-store, recognizable, and understandable way for machines and provide a rich knowledge base for different  ...  Our full investigation gives a comprehensive outlook on several promising research directions for future work.  ...  The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.  ... 
doi:10.1016/j.aiopen.2021.02.002 fatcat:mj2ens2perb5jn5koxdvjmryii

A Gaussian Process Model for Knowledge Propagation in Web Ontologies

Pasquale Minervini, Claudia dAmato, Nicola Fanizzi, Floriana Esposito
2014 2014 IEEE International Conference on Data Mining  
We first identify which relations tend to link similar individuals by means of a finite-set Gaussian Process regression model, and then efficiently propagate knowledge about individuals across their relations  ...  knowledge propagation process. • A method for learning an optimal similarity graph for a given prediction task, by leveraging a set of semantically heterogeneous relations among examples.  ...  A Relations-based Similarity Graph The method for propagating knowledge across similar examples discussed in Sect. III-A relies on a similarity graph, represented by its adjacency matrix W.  ... 
doi:10.1109/icdm.2014.83 dblp:conf/icdm/MinervinidFE14 fatcat:mionnfkodzeyzccjpb432qu23i

Pairwise Constraint Propagation: A Survey [article]

Zhenyong Fu, Zhiwu Lu
2015 arXiv   pre-print
At least two reasons account for this trend: the first is that compared to the data label, pairwise constraints are more general and easily to collect, and the second is that since the available pairwise  ...  As one of the most important types of (weaker) supervised information in machine learning and pattern recognition, pairwise constraint, which specifies whether a pair of data points occur together, has  ...  , with one graph for each of the modalities.  ... 
arXiv:1502.05752v1 fatcat:djagaxttkjawpjfzys2q476zom

Boundary-aware Graph Reasoning for Semantic Segmentation [article]

Haoteng Tang, Haozhe Jia, Weidong Cai, Heng Huang, Yong Xia, Liang Zhan
2021 arXiv   pre-print
In this paper, we propose a Boundary-aware Graph Reasoning (BGR) module to learn long-range contextual features for semantic segmentation.  ...  thereby guide the graph reasoning focus on boundary regions.  ...  To the best of our knowledge, this is the first to use boundary prior knowledge to facilitate graph reasoning for semantic segmentation. (2) An efficient implementation of graph convolutions is developed  ... 
arXiv:2108.03791v1 fatcat:dmlfk2lyvza4bjpbq75isjt4tu

Research on Service Discovery Methods Based on Knowledge Graph

Li Guodong, Qiu Zhang, Yongkai Ding, Wang Zhe
2020 IEEE Access  
Firstly, construct a knowledge graph for service discovery, and then design a template to match the knowledge graph.  ...  This paper makes in-depth research on the key issues of knowledge graph matching for service-oriented discovery.  ...  Graph LSTM makes it possible to learn features directly from examples, instead of creating and adjusting reasoning models.  ... 
doi:10.1109/access.2020.3012670 fatcat:p5e3j6aqrraydflqxddotuhip4

Eligibility Propagation to Speed up Time Hopping for Reinforcement Learning

Petar S. Kormushev, Kohei Nomoto, Fangyan Dong, Kaoru Hirota
2009 Journal of Advanced Computational Intelligence and Intelligent Informatics  
It propagates values from one state to all of its temporal predecessors using a state transitions graph.  ...  Eligibility Propagation provides for Time Hopping similar abilities to what eligibility traces provide for conventional Reinforcement Learning.  ...  This poses a challenge how to efficiently collect, represent and propagate knowledge about actions, rewards, states and transitions.  ... 
doi:10.20965/jaciii.2009.p0600 fatcat:4ensfnhfivf4larfmjpgas2dk4

Guest editorial: Graph learning for computer vision

Qi Wang, Hongkai Yu, Song Wang, Jianzhe Lin
2021 IET Computer Vision  
Graph learning refers to machine learning on graphs, which mainly utilises machine learning algorithms to extract the relevant features of graphs.  ...  Specific tasks include image segmentation, knowledge graph reasoning, clustering, and image classification.  ...  The second paper by Ma et al. entitled Hybrid Attention Mechanism for Few-Shot Relational Learning of Knowledge Graphs, develops a few-shot relationship learning framework.  ... 
doi:10.1049/cvi2.12071 fatcat:hqqnf44n5zgn7k2gkj5zyhqzza
« Previous Showing results 1 — 15 out of 65,839 results