Skip to content
/ Embedding Public
forked from BookmanHan/Embedding

Knowledge Embedding Framework Powered by XiaoHan.

Notifications You must be signed in to change notification settings

shh/Embedding

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Embedding

This Project is contributed by Xiao Han in Tsinghua University.

Datasets

Supported Papers

Citation

Conventionally, if this project helps you, please cite our paper, corresponddingly.

  • Han Xiao, Minlie Huang, Xiaoyan Zhu. From One Point to A Manifold: Orbit Models for Knowledge Graph Embedding. The 25th International Joint Conference on Artificial Intelligence (IJCAI'16).
  • Han Xiao, Minlie Huang, Xiaoyan Zhu. TransG: A Generative Mixture Model for Knowledge Graph Embedding. The 54th Annual Meeting of the Association for Computational Linguistics (ACL'2016).
  • Han Xiao, Minlie Huang, Lian Meng, Xiaoyan Zhu. SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions. The Thirty-First AAAI Conference on Artificial Intelligence (AAAI'17).

Dependency

  • Armadillo
    • Armadillo is a high quality linear algebra library (matrix maths) for the C++ language, aiming towards a good balance between speed and ease of use
    • I bet you could master it, just by scanning the examples.
    • Download URL: http://arma.sourceforge.net/download.html
    • What all you should do is to copy the headers into your environment.
  • Boost
    • C++ Standard Extensive Library.
    • Download URL:http://www.boost.org/users/download/
    • What all you should do is to copy the headers into your environment. Certainly, you could compile the code just as explained in the website.
  • MKL
    • Not Necessary, but I strongly suggest you could take advantage of your devices.

Basic Configuration

  • Windows

    • This project is naturally built on Visual Studio 2013 with Intel C++ Compiler 2016. If we share the same development perference, I guess you could start your work, right now.
    • When you decide to compile it with MSC, there is a little trouble, because you shoud adjust your configuration.
  • Linux / MAC

    • I also apply the Intel C++ Compiler, which could be substituted by GCC, theoretically.
    • icc -std=c++11 -O3 -xHost -qopenmp -m32 Embedding.cpp

Start

  • To justify your data source, please modify the MultiChannelEmbedding\DetailedConfig.hpp.

  • To explore the correspondding method, just fill the template in MultiChannelEmbedding\Embedding.cpp with hyper-parameters.

    • model = new MFactorE(FB15K, LinkPredictionTail, report_path, 10, 0.01, 0.1, 0.01, 10);
    • model->run(500);
    • model->test();
    • delete model;

Alias

  • OrbitE = ManifoldE
  • MFactorE = KSR

About

Knowledge Embedding Framework Powered by XiaoHan.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • C++ 100.0%