
<!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>Embedding approaches</title>

    <link rel="stylesheet" href="../assets/bootstrap/css/bootstrap.min.css">
    <link rel="stylesheet" href="../assets/bootstrap/css/bootstrap-theme.min.css">
    <link rel="stylesheet" href="../assets/css/default.css">
    <link href="https://fonts.googleapis.com/css?family=Crimson+Text|Inconsolata" rel="stylesheet">
    <script src="../assets/jquery/jquery.min.js"></script>
    <script src="../assets/bootstrap/js/bootstrap.min.js"></script>

    <link rel="stylesheet" href="../assets/katex/katex.min.css" media="screen" type="text/css">
    <script src='../assets/katex/katex.min.js' type="text/javascript"></script>
    <script src='../assets/paperjs/dist/paper-full.js' type="text/javascript"></script>
    <script src='../assets/js/parse-bibtex.js' type="text/javascript"></script>
    <script src='../assets/js/chapter.js' type="text/javascript"></script>
      
    
    
    
    
  </head>
  <body>

    

    <div class="container">
      <ul class="nav navbar-nav">
        <li class="dropdown">
          <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="false">&#9776;</a>
          <ul class="dropdown-menu">
            <li><a href="../index.html">Home</a></li>
            <li role="separator" class="divider"></li>           
            <li><a href="01-introduction.html">Introduction</a></li>
            <li><a href="02-overview.html">An overview</a></li>
            <li><a href="03-vectorization.html">Vectorization approaches</a></li>
            <li><a href="03-01-factorization.html">Factorization</a></li>
            <li><a href="03-02-embeddings.html">Neural embeddings</a></li>
            <li><a href="03-03-bayesian-clustering.html">Bayesian clustering</a></li>
            <li><a href="05-relational-approaches.html">Relational approaches</a></li>
            <li><a href="06-hybrid-approaches.html">Hybrid approaches</a></li>
            <li><a href="07-related-approaches.html">Related approaches</a></li>
            <li><a href="08-relational-learning-primer.html">Relational Learning Primer</a></li>
            <li><a href="09-deep-learning-primer.html">Deep Learning Primer</a></li>
          </ul>
        </li>
      </ul>


      <div class="page-header">
        <h1>3.1. Factorization approaches</h1>
      </div>


      


      

      



      <div class="paper">
      	<h2><strong>A Three-Way Model for Collective Learning on Multi-Relational Data</strong></h2>
      	<hr>
      	<div>
      		<div class="paper-authors">
	      	Authors: Maximilian Nickel, Volker Tresp, Hans-Peter Kriegel <br>
	      	Venue: ICML <br>
	      	Year: 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>
      	<div class="paper-break">
      		<br>
      		<hr/>
      	</div>     	
      </div>


      <div class="paper">
        <h2><strong>Factorizing YAGO: Scalable Machine Learning for Linked Data</strong></h2>
        <hr>
        <div>
          <div class="paper-authors">
          Authors: Maximilian Nickel, Volker Tresp, Hans-Peter Kriegel<br>
          Venue: WWW <br>
          Year: 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>
        <div class="paper-break">
          <br>
          <hr/>
        </div>      
      </div>



      <div class="paper">
        <h2><strong>Effective Blending of Two and Three-way Interactions for Modeling Multi-relational Data</strong></h2>
        <hr>
        <div>
          <div class="paper-authors">
          Authors: Alberto García-Durán, Antoine Bordes, Nicolas Usunier<br>
          Venue: ECML-PKDD <br>
          Year: 2014
        </div>
        <div class="paper-resource">
          <a href="https://hal.archives-ouvertes.fr/hal-01131957/document"><strong>Paper</strong></a>
          <br/>
          Code<br/><br/>
        </div>
        </div>
        <div class="paper-break">
          <br>
          <hr/>
        </div>      
      </div>


      <div class="paper">
        <h2><strong>Learning from Latent and Observable Patterns in Multi-Relational Data</strong></h2>
        <hr>
        <div>
          <div class="paper-authors">
          Authors: Maximilian Nickel, Xueyan Jiang, Volker Tresp<br>
          Venue: NIPS <br>
          Year: 2014
        </div>
        <div class="paper-resource">
          <a href="http://papers.nips.cc/paper/5448-reducing-the-rank-in-relational-factorization-models-by-including-observable-patterns.pdf"><strong>Paper</strong></a>
          <br/>
          Code<br/><br/>
        </div>
        </div>
        <div class="paper-break">
          <br>
          <hr/>
        </div>      
      </div>



      <div class="paper">
        <h2><strong>Learning First-Order Logic Embeddings via Matrix Factorization</strong></h2>
        <hr>
        <div>
          <div class="paper-authors">
          Authors: William Yang Wang, William W. Cohen<br>
          Venue: IJCAI <br>
          Year: 2016
        </div>
        <div class="paper-resource">
          <a href="http://www.cs.cmu.edu/~wcohen/postscript/ijcai-2016-yww.pdf"><strong>Paper</strong></a>
          <br/>
          <a href="https://github.com/TeamCohen/ProPPR"><strong>Code</strong></a><br/><br/>
        </div>
        </div>
        <div class="paper-break">
          <br>
          <hr/>
        </div>      
      </div>





      


      




        <p>Next chapter: <a href="03-02-embeddings.html">3.2 NEurla embeddings</a></p>

    </div>

  </body>
</html>
