|
LibKGE – A knowledge graph embedding library for reproducible research
Broscheit, Samuel
;
Ruffinelli, Daniel
;
Kochsiek, Adrian
;
Betz, Patrick
;
Gemulla, Rainer
![[img]](https://madoc.bib.uni-mannheim.de/style/images/fileicons/application_pdf.png) |
PDF
LibKGE – A knowledge graph embedding library for reproducible research.pdf
- Published
Download (437kB)
|
|
DOI:
|
https://doi.org/10.18653/v1/2020.emnlp-demos.22
|
|
URL:
|
https://madoc.bib.uni-mannheim.de/61522
|
|
Additional URL:
|
https://aclanthology.org/2020.emnlp-demos.22/
|
|
URN:
|
urn:nbn:de:bsz:180-madoc-615222
|
|
Document Type:
|
Conference or workshop publication
|
|
Year of publication:
|
2020
|
|
Book title:
|
The 2020 Conference on Empirical Methods in Natural Language Processing : proceedings of systems demonstrations, November 16-20, 2020, EMNLP 2020
|
|
Page range:
|
165-174
|
|
Conference title:
|
The 2020 Conference on Empirical Methods in Natural Language Processing
|
|
Location of the conference venue:
|
Online
|
|
Date of the conference:
|
16.-20.11.2020
|
|
Publisher:
|
Schlangen, David
;
Liu, Qun
|
|
Place of publication:
|
Stroudsburg, PA 18360 USA
|
|
Publishing house:
|
Association for Computational Linguistics (ACL)
|
|
ISBN:
|
978-1-952148-62-0
|
|
Related URLs:
|
|
|
Publication language:
|
English
|
|
Institution:
|
School of Business Informatics and Mathematics > Practical Computer Science I: Data Analytics (Gemulla 2014-)
|
|
Pre-existing license:
|
 Creative Commons Attribution 4.0 International (CC BY 4.0)
|
|
Subject:
|
004 Computer science, internet
|
|
Abstract:
|
LibKGE (https://github.com/uma-pi1/kge) is an open-source PyTorch-based library for training, hyperparameter optimization, and evaluation of knowledge graph embedding models for link prediction. The key goals of LibKGE are to enable reproducible research, to provide a framework for comprehensive experimental studies, and to facilitate analyzing the contributions of individual components of training methods, model architectures, and evaluation methods. LibKGE is highly configurable and every experiment can be fully reproduced with a single configuration file. Individual components are decoupled to the extent possible so that they can be mixed and matched with each other. Implementations in LibKGE aim to be as efficient as possible without leaving the scope of Python/Numpy/PyTorch. A comprehensive logging mechanism and tooling facilitates in-depth analysis. LibKGE provides implementations of common knowledge graph embedding models and training methods, and new ones can be easily added. A comparative study (Ruffinelli et al., 2020) showed that LibKGE reaches competitive to state-of-the-art performance for many models with a modest amount of automatic hyperparameter tuning.
|
|
Additional information:
|
Online-Ressource
|
 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
 | Das Dokument wird vom Publikationsserver der Universitätsbibliothek Mannheim bereitgestellt. |
 Search Authors in
BASE:
Broscheit, Samuel
;
Ruffinelli, Daniel
;
Kochsiek, Adrian
;
Betz, Patrick
;
Gemulla, Rainer
Google Scholar:
Broscheit, Samuel
;
Ruffinelli, Daniel
;
Kochsiek, Adrian
;
Betz, Patrick
;
Gemulla, Rainer
ORCID:
Broscheit, Samuel, Ruffinelli, Daniel, Kochsiek, Adrian, Betz, Patrick and Gemulla, Rainer  ORCID: https://orcid.org/0000-0003-2762-0050
You have found an error? Please let us know about your desired correction here: E-Mail
Actions (login required)
 |
Show item |
|
