LibKGE – A knowledge graph embedding library for reproducible research


Broscheit, Samuel ; Ruffinelli, Daniel ; Kochsiek, Adrian ; Betz, Patrick ; Gemulla, Rainer


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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 > Praktische Informatik I (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

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