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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Bridging Relational and Deep Learning</title>
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<div class="container">
<a href="https://github.com/sebdumancic/RelationalDeepLearning"><img style="position: absolute; top: 0; right: 0; border: 0;" src="https://camo.githubusercontent.com/365986a132ccd6a44c23a9169022c0b5c890c387/68747470733a2f2f73332e616d617a6f6e6177732e636f6d2f6769746875622f726962626f6e732f666f726b6d655f72696768745f7265645f6161303030302e706e67" alt="Fork me on GitHub" data-canonical-src="https://s3.amazonaws.com/github/ribbons/forkme_right_red_aa0000.png"></a>
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<a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="false">☰</a>
<ul class="dropdown-menu">
<li><a href="index.html">Home</a></li>
<li role="separator" class="divider"></li>
<li><a href="chapters/01-introduction.html">Introduction</a></li>
<li><a href="chapters/02-overview.html">An overview</a></li>
<li><a href="chapters/03-vectorization.html">Vectorization approaches</a></li>
<li><a href="chapters/03-01-factorization.html">Factorization</a></li>
<li><a href="chapters/03-02-embeddings.html">Neural embeddings</a></li>
<li><a href="chapters/03-03-bayesian-clustering.html">Bayesian clustering</a></li>
<li><a href="chapters/05-relational-approaches.html">Relational approaches</a></li>
<li><a href="chapters/06-hybrid-approaches.html">Hybrid approaches</a></li>
<li><a href="chapters/07-related-approaches.html">Related approaches</a></li>
<li><a href="chapters/08-relational-learning-primer.html">Relational Learning Primer</a></li>
<li><a href="chapters/09-deep-learning-primer.html">Deep Learning Primer</a></li>
</ul>
</li>
</ul>
<div id="header">
<h1 id="title">Bridging Relational and Deep Learning</h1>
<!---<span class="authors">by Sebastijan Dumančić</span>-->
</div>
<p><br />
This document provides a quick summary ofthe current efforts made towards bridging <strong>relational learning</strong> and <strong>representation or deep learning</strong>. It is made with three objectives in mind:
<ul>
<li> a collection of references addressing the problem of combining relational and deep learning</li>
<li> providing a reading guide to a reader new to the field</li>
<li> <strong>[in progress]</strong> providing a summary of the major ideas and the state-of-the-art</li>
</ul>
</p>
<div id="left">
<h3>Contact</h3>
<ul>
<li>If you have comments, suggestions or noticed something I have missed, feel free to contact me at
<em>sebastijan.dumancic[AT]cs.kuleuven.be</em></li>
</ul>
<h3>Acknowledgments</h3>
<ul>
<li>The HTML outline has been adapted from <a href="https://probmods.org/">Probabilistic Models of Cognition</a></li>
</ul>
<h3>Getting started</h3>
<ul>
<li>If you are new to the field of statistical relational learning, check out <a href="http://www.cs.umd.edu/srl-book/"><strong>Introduction to Statstical Relational Learning</strong></a> and <a href="http://www.springer.com/de/book/9783540200406"><strong>Logical and Relational Learning</strong></a> </li>
<li>If you are new to deep learning, check out <a href="http://www.deeplearningbook.org/">this book</a> </li>
</ul>
<h3>Interesting venues</h3>
<ul>
<li><a href="http://www.starai.org">International Workshop on Statistical Relational AI</a></li>
<li><a href="http://www.neural-symbolic.org/">Workshop series on Neural-Symbolic Learning and Reasoning</a></li>
<li><a href="http://daselab.cs.wright.edu/nesy/CoCo2016/index.html">Cognitive Computation: Integrating Neural and Symbolic Approaches</a></li>
<li><a href="http://www.akbc.ws/2016/">Workshop on Automated Knowledge Base Construction</a></li>
</ul>
</div>
<div id="right">
<h3>Chapters</h3>
<ol>
<li>
<a href="#vectorizationapproahces">Vectorization approaches</a><br />
<em>Mapping instances and relationships to vectors and matrices.</em>
</li>
<ol>
<li><a href="#factorization">Factorization</a><br /></li>
<li><a href="#embeddings">Neural embeddings</a><br /></li>
<li><a href="#regularizingembeddings">Regularizing embeddings</a></li>
</ol>
<li>
<a href="#relationalapproaches">Relational approaches</a><br />
<em>Constructing relational feature hierarchies.</em>
</li>
<li>
<a href="#hybridapproaches">Hybrid approaches</a><br />
<em>Approaches that combine aspects of embedding and relational approaches.</em>
<ul>
<li><a href="#relationalfeaturesinanns">Feeding relational features to ANN</a></li>
<li><a href="#templatinganns">Templating ANNs</a></li>
<li><a href="#otherhyberidapproaches">Other approaches</a></li>
</ul>
</li>
<li>
<a href="#relatedappraoches">Related approaches</a><br />
<em>Approaches that could be extended to relational data.</em>
</li>
</ol>
<br>
<br>
</div>
<br>
<br/>
<br>
<br>
<hr>
<hr>
</div>
<div class="container">
<section id="vectorizationapproahces">
<h2><strong>Vectorization approaches</strong></h2>
<hr>
<section id="factorization">
<h3>Factorization approaches</h3>
<div class="paper">
<strong>Modelling Relational Data using Bayesian Clustered Tensor Factorization</strong>
<div>
<div class="paper-authors">
Ilya Sutskever, Joshua B. Tenenbaum, Ruslan R. Salakhutdinov<br>
NIPS 2009
</div>
<div class="paper-resource">
<a href="https://papers.nips.cc/paper/3863-modelling-relational-data-using-bayesian-clustered-tensor-factorization.pdf"><strong>Paper</strong></a>
<br/>
Code<br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>A Three-Way Model for Collective Learning on Multi-Relational Data</strong>
<div>
<div class="paper-authors">
Maximilian Nickel, Volker Tresp, Hans-Peter Kriegel <br>
ICML 2011
</div>
<div class="paper-resource">
<a href="http://www.icml-2011.org/papers/438_icmlpaper.pdf"><strong>Paper</strong></a>
<br/>
<a href="https://github.com/mnick/rescal.py"><strong>Code</strong></a><br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Factorizing YAGO: Scalable Machine Learning for Linked Data</strong>
<div>
<div class="paper-authors">
Maximilian Nickel, Volker Tresp, Hans-Peter Kriegel<br>
WWW 2011
</div>
<div class="paper-resource">
<a href="https://pdfs.semanticscholar.org/498c/a0a1f8c980586408addf7ab2919ecdb7dd3d.pdf"><strong>Paper</strong></a>
<br/>
Code<br/><br/>
</div>
</div>
<hr/>
</div>
<hr>
</section>
<section id="embeddings">
<h3>Neural embeddings</h3>
<div class="paper">
<strong>Learning Structured Embeddings of Knowledge Bases</strong>
<div>
<div class="paper-authors">
Antoine Bordes, Jason Weston, Ronan Collobert, Yoshua Bengio<br>
AAAI 2011
</div>
<div class="paper-resource">
<a href="http://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/viewFile/3659/3898"><strong>Paper</strong></a>
<br/>
<a href="https://github.com/glorotxa/SME"><strong>Code</strong></a>
<br><br><br>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>A latent factor model for highly multi-relational data</strong>
<div>
<div class="paper-authors">
Rodolphe Jenatton, Nicolas L Roux, Antoine Bordes, Guillaume R Obozinski <br>
NIPS 2012
</div>
<div class="paper-resource">
<a href="http://papers.nips.cc/paper/4744-a-latent-factor-model-for-highly-multi-relational-data.pdf"><strong>Paper</strong></a>
<br/>
<a href="https://everest.hds.utc.fr/doku.php?id=en:lfmnips12"><strong>Code</strong></a><br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>A Semantic Matching Energy Function for Learning with Multi-relational Data</strong>
<div>
<div class="paper-authors">
Antoine Bordes, Xavier Glorot, Jason Weston, Yoshua Bengio<br>
Machine Learning 2013: Special Issue on Learning Semantics
</div>
<div class="paper-resource">
<a href="http://www.iro.umontreal.ca/~lisa/publications2/index.php/publications/show/568"><strong>Paper</strong></a>
<br/>
<a href="https://github.com/glorotxa/SME"><strong>Code</strong></a><br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Translating Embeddings for Modeling Multi-relational Data</strong>
<div>
<div class="paper-authors">
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko<br>
NIPS 2013
</div>
<div class="paper-resource">
<a href="https://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data"><strong>Paper</strong></a>
<br/>
<a href="https://github.com/glorotxa/SME"><strong>Code</strong></a><br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Reasoning With Neural Tensor Networks for Knowledge Base Completion</strong>
<div>
<div class="paper-authors">
Richard Socher, Danqi Chen, Christopher D. Manning, Andrew Ng<br>
- 2013
</div>
<div class="paper-resource">
<a href="https://papers.nips.cc/paper/5028-reasoning-with-neural-tensor-networks-for-knowledge-base-completion.pdf"><strong>Paper</strong></a>
<br/>
<a href="https://github.com/dddoss/tensorflow-socher-ntn"><strong>Code</strong></a><br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss</strong>
<div>
<div class="paper-authors">
Ben London, Theodoros Rekatsinas, Bert Huang, Lise Getoor<br>
NIPS 2013
</div>
<div class="paper-resource">
<a href="https://pdfs.semanticscholar.org/0978/79a401b4083b769c754acfca115ae9fd5552.pdf"><strong>Paper</strong></a>
<br/>
<a href="https://github.com/dddoss/tensorflow-socher-ntn"><strong>Code</strong></a><br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Low-Dimensional Embeddings of Logic</strong>
<div>
<div class="paper-authors">
Tim Rocktaschel, Matko Bosnjak, Sameer Singh, Sebastian Riedel<br>
ACL Workshop on Semantic Parsin 2014
</div>
<div class="paper-resource">
<a href="http://mr.cs.ucl.ac.uk/publications/pdfs/rocktaeschel14low.pdf"><strong>Paper</strong></a>
<br/>
<a href="http://yoavartzi.com/sp14/slides/rockt.sp14.pdf"><strong>Code</strong></a><br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Learning Entity and Relation Embeddings for Knowledge Graph Completion</strong>
<div>
<div class="paper-authors">
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, Xuan Zhu<br>
AAAI 2015
</div>
<div class="paper-resource">
<a href="http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/viewFile/9571/9523"><strong>Paper</strong></a>
<br/>
<a href="https://github.com/thunlp/KB2E"><strong>Code</strong></a><br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Compositional Vector Space Models for Knowledge Base Completion</strong>
<div>
<div class="paper-authors">
Arvind Neelakantan, Benjamin Roth, Andrew McCallum<br>
ACL 2015
</div>
<div class="paper-resource">
<a href="http://www.aclweb.org/anthology/P15-1016"><strong>Paper</strong></a>
<br/>
Code<br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Embedding Entities and Relations for Learning and Inference in Knowledge Bases</strong>
<div>
<div class="paper-authors">
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, Li Deng<br>
ICLR 2015
</div>
<div class="paper-resource">
<a href="https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/ICLR2015_updated.pdf"><strong>Paper</strong></a>
<br/>
Code<br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Modeling Relation Paths for Representation Learning of Knowledge Bases</strong>
<div>
<div class="paper-authors">
Yankai Lin, Zhiyuan Liu, Huanbo Luan, Maosong Sun, Siwei Rao, Song Liu<br>
EMNLP 2015
</div>
<div class="paper-resource">
<a href="http://www.emnlp2015.org/proceedings/EMNLP/pdf/EMNLP082.pdf"><strong>Paper</strong></a>
<br/>
Code</a><br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Composing Relationships with Translations</strong>
<div>
<div class="paper-authors">
Alberto García-Durán, Antoine Bordes, Nicolas Usunier<br>
EMNLP 2015
</div>
<div class="paper-resource">
<a href="https://pdfs.semanticscholar.org/a75f/c6eacc0f0b2d22f28762658ee759bd1975b4.pdf?_ga=2.140373619.1786246857.1500222216-335068395.1500222216"><strong>Paper</strong></a>
<br/>
Code</a><br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Traversing Knowledge Graphs in Vector Space</strong>
<div>
<div class="paper-authors">
Kelvin Guu, John Miller, Percy Liang<br>
EMNLP 2015
</div>
<div class="paper-resource">
<a href="https://arxiv.org/pdf/1506.01094.pdf"><strong>Paper</strong></a>
<br/>
<a href="https://github.com/millerjohnp/traversing_knowledge_graphs">Code</a><br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Representation Learning of Knowledge Graphs with Entity Descriptions</strong>
<div>
<div class="paper-authors">
Ruobing Xie, Zhiyuan Liu, Jia Jia, Huanbo Luan, Maosong Sun<br>
AAAI 2016
</div>
<div class="paper-resource">
<a href="http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12216"><strong>Paper</strong></a>
<br/>
<a href="https://github.com/thunlp/DKRL"><strong>Code</strong></a><br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Combining two and three-way embedding models for link prediction in knowledge bases</strong>
<div>
<div class="paper-authors">
Alberto García-Durán, Antoine Bordes, Nicolas Usunier, Yves Grandvalet<br>
JAIR 2016
</div>
<div class="paper-resource">
<a href="https://www.jair.org/media/5013/live-5013-9208-jair.pdf"><strong>Paper</strong></a>
<br/>
<a href="https://github.com/glorotxa/SME"><strong>Code</strong></a><br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Complex Embeddings for Simple Link Prediction</strong>
<div>
<div class="paper-authors">
Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, Guillaume Bouchard<br>
ICML 2016
</div>
<div class="paper-resource">
<a href="http://jmlr.org/proceedings/papers/v48/trouillon16.pdf"><strong>Paper</strong></a>
<br/>
<a href="https://github.com/ttrouill/complex"><strong>Code</strong></a><br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Efficient energy-based embedding models for link prediction in knowledge graphs</strong>
<div>
<div class="paper-authors">
Pasquale Minervini, Claudia d’Amato, Nicola Fanizzi<br>
Journal of Intelligent Information Systems 2016
</div>
<div class="paper-resource">
<a href="https://link.springer.com/article/10.1007/s10844-016-0414-7"><strong>Paper</strong></a>
<br/>
<a href="https://github.com/pminervini/ebemkg"><strong>Code</strong></a><br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Representation Learning of Knowledge Graphs with Hierarchical Types</strong>
<div>
<div class="paper-authors">
Ruobing Xie, Zhiyuan Liu, Maosong Sun<br>
IJCAI 2016
</div>
<div class="paper-resource">
<a href="https://www.ijcai.org/Proceedings/16/Papers/421.pdf"><strong>Paper</strong></a>
<br/>
<a href="https://github.com/thunlp/TKRL"><strong>Code</strong></a><br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Holographic Embeddings Knowledge Graphs</strong>
<div>
<div class="paper-authors">
Maximilian Nickel, Lorenzo Rosasco, Tomaso Poggio<br>
AAAI 2016
</div>
<div class="paper-resource">
<a href="https://arxiv.org/pdf/1510.04935.pdf"><strong>Paper</strong></a>
<br/>
<a href="https://github.com/mnick/holographic-embeddings"><strong>Code</strong></a><br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>STransE: a novel embedding model of entities and relationships in knowledge bases</strong>
<div>
<div class="paper-authors">
Dat Quoc Nguyen, Kairit Sirts, Lizhen Qu and Mark Johnson<br>
NAACL-HLT 2016
</div>
<div class="paper-resource">
<a href="http://www.aclweb.org/anthology/N16-1054"><strong>Paper</strong></a>
<br/>
<a href="https://github.com/datquocnguyen/STransE"><strong>Code</strong></a><br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Compositional Learning of Embeddings for Relation Paths in Knowledge Bases and Text</strong>
<div>
<div class="paper-authors">
Kristina Toutanova, Victoria Lin, Wen-tau Yih, Hoifung Poon, Chris Quirk<br>
ACL 2016
</div>
<div class="paper-resource">
<a href="https://www.semanticscholar.org/paper/Compositional-Learning-of-Embeddings-for-Relation-Toutanova-Lin/7f8394308f12209dcb459a5be21aa81767dab7b7"><strong>Paper</strong></a>
<br/>
Code<br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>TransG: A Generative Mixture Model for Knowledge Graph Embedding</strong>
<div>
<div class="paper-authors">
Han Xiao, Minlie Huang, Xiaoyan Zhu<br>
ACL 2016
</div>
<div class="paper-resource">
<a href="https://aclweb.org/anthology/P/P16/P16-1219.pdf"><strong>Paper</strong></a>
<br/>
<a href="https://github.com/BookmanHan/Embedding"><strong>Code</strong></a> <br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Deductive and Analogical Reasoning on a Semantically Embedded Knowledge Graph</strong>
<div>
<div class="paper-authors">
Douglas Summers-Stay<br>
AGI 2017
</div>
<div class="paper-resource">
<a href="https://arxiv.org/pdf/1707.03232.pdf"><strong>Paper</strong></a>
<br/>
Code<br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Inductive Representation Learning on Large Graphs</strong>
<div>
<div class="paper-authors">
William L. Hamilton, Rex Ying, Jure Leskovec<br>
NIPS 2017
</div>
<div class="paper-resource">
<a href="https://arxiv.org/pdf/1706.02216.pdf"><strong>Paper</strong></a>
<br/>
<a href="https://github.com/williamleif/GraphSAGE"><strong>Code</strong></a><br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Distributed representation learning for knowledge graphs with entity descriptions</strong>
<div>
<div class="paper-authors">
Miao Fan, Qiang Zhou, Thomas Fang Zheng, Ralph Grishman<br>
Pattern Recognition Letters 2017
</div>
<div class="paper-resource">
<a href="http://www.sciencedirect.com/science/article/pii/S0167865516302380"><strong>Paper</strong></a>
<br/>
Code<br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Analogical Inference for Multi-Relational Embeddings</strong>
<div>
<div class="paper-authors">
Hanxiao Liu, Yuexin Wu, Yiming Yang<br>
ICML 2017
</div>
<div class="paper-resource">
<a href="https://arxiv.org/pdf/1705.02426.pdf"><strong>Paper</strong></a>
<br/>
<a href="https://github.com/quark0/ANALOGY"><strong>Code</strong></a><br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Learning Continuous Semantic Representations of Symbolic Expressions</strong>
<div>
<div class="paper-authors">
Miltiadis Allamanis, Pankajan Chanthirasegaran, Pushmeet Kohli, Charles Sutton<br>
ICML 2017
</div>
<div class="paper-resource">
<a href="https://arxiv.org/pdf/1611.01423.pdf"><strong>Paper</strong></a>
<br/>
<a href="https://github.com/mast-group/eqnet"><strong>Code</strong></a><br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>An Interpretable Knowledge Transfer Model for Knowledge Base Completion</strong>
<div>
<div class="paper-authors">
Qizhe Xie, Xuezhe Ma, Zihang Dai, Eduard Hovy<br>
ACL 2017
</div>
<div class="paper-resource">
<a href="https://arxiv.org/pdf/1704.05908.pdf"><strong>Paper</strong></a>
<br/>
Code<br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Learning Graph Representations with Embedding Propagation</strong>
<div>
<div class="paper-authors">
Alberto Garcia-Duran, Mathias Niepert<br>
NIPS 2017
</div>
<div class="paper-resource">
<a href="https://arxiv.org/pdf/1710.03059.pdf"><strong>Paper</strong></a>
<br/>
Code<br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>2D Convolutional Graph Embeddings</strong>
<div>
<div class="paper-authors">
Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel<br>
-
</div>
<div class="paper-resource">
<a href="https://arxiv.org/pdf/1707.01476.pdf"><strong>Paper</strong></a>
<br/>
<a href="https://github.com/TimDettmers/ConvE"><strong>Code</strong></a><br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Learning Knowledge Graph Embeddings with Type Regularizer</strong>
<div>
<div class="paper-authors">
Bhushan Kotnis, Vivi Nastase<br>
-
</div>
<div class="paper-resource">
<a href="https://arxiv.org/abs/1706.09278"><strong>Paper</strong></a>
<br/>
Code<br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Poincare Embeddings for Learning Hierarchical Representations</strong>
<div>
<div class="paper-authors">
Maximilian Nickel, Douwe Kiela<br>
-
</div>
<div class="paper-resource">
<a href="https://arxiv.org/pdf/1705.08039.pdf"><strong>Paper</strong></a>
<br/>
Code<br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Deep Gaussian Embedding of Attributed Graphs: Unsupervised Inductive Learning via Ranking</strong>
<div>
<div class="paper-authors">
Aleksandar Bojchevski, Stephan Günnemann<br>
-
</div>
<div class="paper-resource">
<a href="https://arxiv.org/pdf/1707.03815.pdf"><strong>Paper</strong></a>
<br/>
Code<br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>HARP: Hierarchical Representation Learning for Networks</strong>
<div>
<div class="paper-authors">
Haochen Chen, Bryan Perozzi, Yifan Hu, Steven Skiena<br>
-
</div>
<div class="paper-resource">
<a href="https://arxiv.org/pdf/1706.07845.pdf"><strong>Paper</strong></a>
<br/>
Code<br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Modeling Relational Data with Graph Convolutional Networks</strong>
<div>
<div class="paper-authors">
Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling<br>
-
</div>
<div class="paper-resource">
<a href="https://arxiv.org/pdf/1703.06103.pdf"><strong>Paper</strong></a>
<br/>
Code<br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Deep Generative Models for Relational Data with Side Information</strong>
<div>
<div class="paper-authors">
Changwei Hu, Piyush Rai, Lawrence Carin<br>
-
</div>
<div class="paper-resource">
<a href="https://arxiv.org/pdf/1706.05136.pdf"><strong>Paper</strong></a>
<br/>
Code<br/><br/>
</div>
</div>
<hr/>
</div>
<hr>
</section>
<section id="regularizingembeddings">
<h4>Regularizing embeddings</h4>
<div class="paper">
<strong>Injecting Logical Background Knowledge into Embeddings for Relation Extraction</strong>
<div>
<div class="paper-authors">
Tim Rocktaschel, Sameer Singh, Sebastian Riedel<br>
NAACL 2015
</div>
<div class="paper-resource">
<a href="https://rockt.github.io/pdf/rocktaschel2015injecting.pdf"><strong>Paper</strong></a>
<br/>
<a href="https://github.com/uclmr/low-rank-logic"><strong>Code</strong></a><br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Knowledge base completion using embeddings and rules</strong>
<div>
<div class="paper-authors">
Quan Wang, Bin Wang, Li Guo<br>
IJCAI 2015
</div>
<div class="paper-resource">
<a href="https://www.ijcai.org/Proceedings/15/Papers/264.pdf"><strong>Paper</strong></a>
<br/>
Code</a><br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Entity embeddings with conceptual subspaces as a basis for plausible reasoning</strong>
<div>
<div class="paper-authors">
Shoaib Jameel, Steven Schockaert<br>
ECAI 2016
</div>
<div class="paper-resource">
<a href="https://arxiv.org/pdf/1602.05765.pdf"><strong>Paper</strong></a>
<br/>
Code<br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Regularizing Knowledge Graph Embeddings via Equivalence and Inversion Axioms</strong>
<div>
<div class="paper-authors">
Pasquale Minervini, Luca Costabello, Emir Muñoz, Vít Nováček, Pierre-Yves Vandenbussche<br>
ECML PKDD 2017
</div>
<div class="paper-resource">
<a href="https://luca.costabello.info/docs/ECML_PKDD_2017_embeddings.pdf"><strong>Paper</strong></a>
<br/>
<a href="https://github.com/pminervini/neural-schema-regularization"><strong>Code</strong></a><br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Adversarial Sets for Regularising Neural Link Predictors</strong>
<div>
<div class="paper-authors">
Pasquale Minervini, Thomas Demeester, Tim Rocktäschel, Sebastian Riedel<br>
UAI 2017
</div>
<div class="paper-resource">
<a href="https://arxiv.org/pdf/1707.07596.pdf"><strong>Paper</strong></a>
<br/>
<a href="https://github.com/uclmr/inferbeddings"><strong>Code</strong></a><br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Learning Knowledge Graph Embeddings with Type Regularizer</strong>
<div>
<div class="paper-authors">
Bhushan Kotnis, Vivi Nastase<br>
-
</div>
<div class="paper-resource">
<a href="https://arxiv.org/pdf/1706.09278.pdf"><strong>Paper</strong></a>
<br/>
<a href="https://github.com/bhushank/kge"><strong>Code</strong></a><br/><br/>
</div>
</div>
<hr/>
</div>
</section>
</section>
</div>
<div class="container">
<section id="relationalapproaches">
<h2><strong>Relational approaches</strong></h2>
<hr>
<div class="paper">
<strong>Change of representation for statistical relational learning</strong>
<div>
<div class="paper-authors">
Jesse Davis, Irene Ong, Jan Struyf, Elizabeth Burnside, David Page, Vıtor Santos Costa<br>
IJCAI'07
</div>
<div class="paper-resource">
<a href="http://pages.cs.wisc.edu/~jdavis/davis.pdf"><strong>Paper</strong></a>
<br/>
Code<br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>Introducing DRAIL: a Step Towards Declarative Deep Relational Learning</strong>
<div>
<div class="paper-authors">
Xiao Zhang, Maria Leonor Pacheco, Chang Li, Dan Goldwasser <br>
Structured Prediction for NLP 2016
</div>
<div class="paper-resource">
<a href="https://www.semanticscholar.org/paper/Introducing-DRAIL-a-Step-Towards-Declarative-Deep-Zhang-Pacheco/fab31cbb5f1964a07a2feffb9febe0ea8af67f68"><strong>Paper</strong></a>
<br/>
Code<br/><br/>
</div>
</div>
<hr/>
</div>
<div class="paper">
<strong>A Clustering-based Relational Representation Learning with an Explicit Distributed Representation</strong>