Skip to content

scpei/SEA

master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
run
 
 
src
 
 
 
 

Semi-Supervised Entity Alignment via Knowledge Graph Embedding with Awareness of Degree Difference

The study in this paper focuses on two important issues that limit the accuracy of current entity alignment solutions:

  1. labeled data of priorly aligned entity pairs are difficult and expensive to acquire, whereas abundant of unlabeled data are not used;
  2. knowledge graph embedding is affected by entity’s degree difference, which brings challenges to align high frequent and low frequent entities.

The implementation is based on the code and data of MTransE.

This version is based on entity-level alignment, instead of triple-level alignment.

Contact: Shichao Pei (shichao.pei@kaust.edu.sa)

Usage:

To run the code, you need to have Python3 and Tensorflow installed.

run run_train_test.sh

Visit https://drive.google.com/file/d/1AsPPU4ka1Rc9u-XYMGWtvV65hF3egi0z/view to download the datasets.

Dependencies

  • Python>=3.5
  • Tensorflow>=1.1.0
  • numpy
  • scipy
  • multiprocessing
  • pickle
  • heapq

Reference

Please refer to our paper.

@inproceedings{pei2019semi,
  title={Semi-supervised entity alignment via knowledge graph embedding with awareness of degree difference},
  author={Pei, Shichao and Yu, Lu and Hoehndorf, Robert and Zhang, Xiangliang},
  booktitle={The World Wide Web Conference},
  year={2019}
}

About

Code of SEA (WWW2019)

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published