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Variational Bayes in Private Settings (VIPS) (Extended Abstract)

James R. Foulds, Mijung Park, Kamalika Chaudhuri, Max Welling
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
In the full paper we extend our method to a broad class of models, including Bayesian logistic regression and sigmoid belief networks.  ...  We introduce a general privacy-preserving framework for Variational Bayes (VB), a widely used optimization-based Bayesian inference method.  ...  [Du and Tong, 2019] shows multi-resolution embedding on multiple networks brings further improvement on network alignment.  ... 
doi:10.24963/ijcai.2020/694 dblp:conf/ijcai/YanYH20 fatcat:pc4nelo7gzfmvmsiym3ohwspxa

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  ...  Entity alignment aims to find equivalence relations between entities in different knowledge graphs but semantically represent the same real-world object, which is the most fundamental and essential technology  ...  multi-view information for entity alignment.  ... 
doi:10.1016/j.aiopen.2021.02.002 fatcat:mj2ens2perb5jn5koxdvjmryii

How to Train Your Agent to Read and Write [article]

Li Liu, Mengge He, Guanghui Xu, Mingkui Tan, Qi Wu
2021 arXiv   pre-print
In this paper, we propose a Deep ReAder-Writer (DRAW) network, which consists of a Reader that can extract knowledge graphs (KGs) from input paragraphs and discover potential knowledge, a graph-to-text  ...  However, it is difficult for new researchers (students) to fully grasp this ability.  ...  Our Reviewer consists of three modules to review and evaluate whether the generated paragraphs are real and to align with the given KGs, in order to improve the text generation ability.  ... 
arXiv:2101.00916v1 fatcat:hg6wq4ugjza7xizp2jy5su3tba

Link-Intensive Alignment for Incomplete Knowledge Graphs [article]

Vinh Van Tong, Thanh Trung Huynh, Thanh Tam Nguyen, Hongzhi Yin, Quoc Viet Hung Nguyen, Quyet Thang Huynh
2021 arXiv   pre-print
Knowledge graph (KG) alignment - the task of recognizing entities referring to the same thing in different KGs - is recognized as one of the most important operations in the field of KG construction and  ...  We also demonstrate that the knowledge exchanging between the KGs helps reveal the unseen facts from knowledge graphs (a.k.a. knowledge completion), with the result being 3.5\% higher than the SOTA knowledge  ...  Song, “Variational multi-order convolutional networks,” IEEE Transactions on Knowledge reasoning for question answering with  ... 
arXiv:2112.09266v1 fatcat:5xb2b23w3bed7km4opfm6usgzi

Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning [article]

Zhengyao Jiang, Pasquale Minervini, Minqi Jiang, Tim Rocktaschel
2021 arXiv   pre-print
Convolution Network (R-GCN).  ...  We show that, with GTG, R-GCNs generalize better both in terms of in-distribution and out-of-distribution compared to baselines based on Convolutional Neural Networks and Neural Logic Machines on challenging  ...  We thank Edward Grefenstette and the anonymous reviewers for their insightful feedback.  ... 
arXiv:2102.04220v1 fatcat:ryjhh6xr2zayrak3mnj6pmoixu

Text Generation from Knowledge Graphs with Graph Transformers [article]

Rik Koncel-Kedziorski, Dhanush Bekal, Yi Luan, Mirella Lapata, and Hannaneh Hajishirzi
2019 arXiv   pre-print
In this work, we address the problem of generating coherent multi-sentence texts from the output of an information extraction system, and in particular a knowledge graph.  ...  We introduce a novel graph transforming encoder which can leverage the relational structure of such knowledge graphs without imposing linearization or hierarchical constraints.  ...  We also thank the anonymous reviewers and the UW-NLP group for their helpful comments.  ... 
arXiv:1904.02342v2 fatcat:5kvtaaa47bhqhpa2frbuptebsu

A Survey on Knowledge Graphs: Representation, Acquisition and Applications [article]

Shaoxiong Ji and Shirui Pan and Erik Cambria and Pekka Marttinen and Philip S. Yu
2021 IEEE Transactions on Neural Networks and Learning Systems   accepted
For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning, are reviewed.  ...  Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction towards cognition and human-level intelligence.  ...  There are also more datasets for specific tasks such as cross-lingual DBP15K [128] and DWY100K [127] for entity alignment, multi-view knowledge graphs of YAGO26K-906 and DB111K-174 [119] with instances  ... 
doi:10.1109/tnnls.2021.3070843 pmid:33900922 arXiv:2002.00388v4 fatcat:4l2yxnf3wbg4zpzdumduvyr4he

Graph Neural Networks for Natural Language Processing: A Survey [article]

Lingfei Wu, Yu Chen, Kai Shen, Xiaojie Guo, Hanning Gao, Shucheng Li, Jian Pei, Bo Long
2021 arXiv   pre-print
To the best of our knowledge, this is the first comprehensive overview of Graph NeuralNetworks for Natural Language Processing.  ...  In this survey, we present a comprehensive overview onGraph Neural Networks(GNNs) for Natural Language Processing.  ...  Aligning cross-lingual entities with multi- aspect information.  ... 
arXiv:2106.06090v1 fatcat:zvkhinpcvzbmje4kjpwjs355qu

DKN: Deep Knowledge-Aware Network for News Recommendation [article]

Hongwei Wang, Fuzheng Zhang, Xing Xie, Minyi Guo
2018 arXiv   pre-print
The key component of DKN is a multi-channel and word-entity-aligned knowledge-aware convolutional neural network (KCNN) that fuses semantic-level and knowledge-level representations of news.  ...  KCNN treats words and entities as multiple channels, and explicitly keeps their alignment relationship during convolution.  ...  Being aware of the above limitations, we propose a multi-channel and word-entity-aligned KCNN for combining word semantics and knowledge information.  ... 
arXiv:1801.08284v2 fatcat:c6p7njibivfsricpgrxin2nj2u

Neural relation extraction: a review

2020 Turkish Journal of Electrical Engineering and Computer Sciences  
For creating distant supervision datasets such as NYT, entity 23 pairs in a triple are aligned with the sentences that contain head and tail entities in the natural text.  ...  A triple (h, r , t ) implies that entity h has relation r with another entity t . Knowledge graphs (KG) 18 such as FreeBase [4] and DBpedia [2] are examples of such representations.  ...  GraphRel, introduced by Fu et al. [16] is a graph convolutional network based neural 8 model that jointly learns entities and relations.  ... 
doi:10.3906/elk-2005-119 fatcat:o36duadbunhmbesuyayc5jfmxe

Knowledge Graph Representation Learning with Multi-Scale Capsule-based Embedding Model Incorporating Entity Descriptions

Jingwei Cheng, Fu Zhang, Zhi Yang
2020 IEEE Access  
Multi-Scale Capsule-based Embedding (MCapsE) [24] further extends CapsE with multi-scale convolution kernels in the convolution layer to extract features at different abstract levels.  ...  INTRODUCTION A Knowledge Graph (KG) is a graph of data intended to accumulate and convey knowledge of the real world, whose nodes represent entities of interest and whose edges represent relations between  ... 
doi:10.1109/access.2020.3035636 fatcat:zb66voi2abcsbew7kadhtczawi

Knowledge Representation via Joint Learning of Sequential Text and Knowledge Graphs [article]

Jiawei Wu, Ruobing Xie, Zhiyuan Liu, Maosong Sun
2016 arXiv   pre-print
Given each reference sentence of an entity, we first utilize recurrent neural network with pooling or long short-term memory network to encode the semantic information of the sentence with respect to the  ...  There are two main challenges for constructing knowledge representations from plain texts: (1) How to take full advantages of sequential contexts of entities in plain texts for KRL. (2) How to dynamically  ...  ., 2015 ) extends the alignment model by considering entity descriptions.  ... 
arXiv:1609.07075v1 fatcat:yr3hgolnm5g2xfawknhriarhoy

Neural, Symbolic and Neural-Symbolic Reasoning on Knowledge Graphs [article]

Jing Zhang, Bo Chen, Lingxi Zhang, Xirui Ke, Haipeng Ding
2021 arXiv   pre-print
We also briefly discuss the future directions for knowledge graph reasoning.  ...  Since knowledge graphs can be viewed as the discrete symbolic representations of knowledge, reasoning on knowledge graphs can naturally leverage the symbolic techniques.  ...  They first transform a question into a multi-constraint query graph, then propose a Siamese convolutional neural networks to calculate the similarity between the query graph and the input natural language  ... 
arXiv:2010.05446v5 fatcat:tc6fowebkzbv7df3cjyhkcu6uq

A Survey of Embedding Space Alignment Methods for Language and Knowledge Graphs [article]

Alexander Kalinowski, Yuan An
2020 arXiv   pre-print
To this end, we survey the current research landscape on word, sentence and knowledge graph embedding algorithms.  ...  Neural embedding approaches have become a staple in the fields of computer vision, natural language processing, and more recently, graph analytics.  ...  More recently, attention has turned to using graph convolutional networks [39] (GCNs) for knowledge graph embeddings.  ... 
arXiv:2010.13688v1 fatcat:npkzwukih5gwnkvng2fxy7ls5y

Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey [article]

Tapas Nayak and Navonil Majumder and Pawan Goyal and Soujanya Poria
2021 arXiv   pre-print
Regarding neural architectures, we cover convolutional models, recurrent network models, attention network models, and graph convolutional models in this survey.  ...  Recently, with the advances made in continuous representation of words (word embeddings) and deep neural architectures, many research works are published in the area of relation extraction and it is very  ...  proposed multi-head attention guided graph convolution network and Li et al. (2020a) proposed GCN-based dual attention network for document level relation extraction.  ... 
arXiv:2103.16929v1 fatcat:a25435weifccdknduaiilk7ufy
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