short-paper

NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs

Authors:

Huajun ChenAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 3323 - 3328

https://doi.org/10.1145/3477495.3531669

Published: 07 July 2022 Publication History

Abstract

NeuralKG is an open-source Python-based library for diverse representation learning of knowledge graphs. It implements three kinds of Knowledge Graph Embedding (KGE) methods, including conventional KGEs, GNN-based KGEs, and Rule-based KGEs. With a unified framework, NeuralKG successfully reproduces link prediction results of these methods on benchmarks, freeing users from the laborious task of reimplementing them, especially for some methods originally written in non-python programming languages. Besides, NeuralKG is highly configurable and extensible. It provides various decoupled modules that can be mixed and adapted to each other. Thus with NeuralKG, developers and researchers can quickly implement their own designed models and obtain the optimal training methods to achieve the best performance efficiently. We built a website http://neuralkg.zjukg.org to organize an open and shared KG representation learning community. The library, experimental methodologies, and model reimplement results of NeuralKG are all publicly released at https://github.com/zjukg/NeuralKG.

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Cited By

Wang JWu TChen SLiu YZhu SLi WXu JQi GHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)unKR: A Python Library for Uncertain Knowledge Graph Reasoning by Representation LearningProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657661(2822-2826)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657661
Feng XLiu XYang YWang WWang J(2024)Learning Rules in Knowledge Graphs via Contrastive LearningDatabase Systems for Advanced Applications10.1007/978-981-97-5562-2_26(408-424)Online publication date: 27-Oct-2024
https://doi.org/10.1007/978-981-97-5562-2_26
Zhang WYao ZChen MHuang ZChen HChen HDuh WHuang HKato MMothe JPoblete B(2023)NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation LearningProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591809(3140-3144)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591809
Show More Cited By

Index Terms

NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
2. Information systems

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cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

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Spotify
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RMIT University
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Gabriella Kazai
Waseda University

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Published: 07 July 2022

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NFSCU
Zhejiang Provincial Natural Science Foundation of China
Ningbo Natural Science Foundation
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SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

Madrid, Spain

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Cited By

Wang JWu TChen SLiu YZhu SLi WXu JQi GHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)unKR: A Python Library for Uncertain Knowledge Graph Reasoning by Representation LearningProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657661(2822-2826)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657661
Feng XLiu XYang YWang WWang J(2024)Learning Rules in Knowledge Graphs via Contrastive LearningDatabase Systems for Advanced Applications10.1007/978-981-97-5562-2_26(408-424)Online publication date: 27-Oct-2024
https://doi.org/10.1007/978-981-97-5562-2_26
Zhang WYao ZChen MHuang ZChen HChen HDuh WHuang HKato MMothe JPoblete B(2023)NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation LearningProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591809(3140-3144)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591809
Liu YZhang RChen J(2023)Is Averaging Always the Best? Improving Aggregation Method for Federated Knowledge Graph Embedding2023 IEEE International Conference on Networking, Sensing and Control (ICNSC)10.1109/ICNSC58704.2023.10318978(1-6)Online publication date: 25-Oct-2023
https://doi.org/10.1109/ICNSC58704.2023.10318978
Jia N(2023)ASLEEP: A Shallow neural modEl for knowlEdge graph comPletionNeural Information Processing10.1007/978-981-99-1642-9_9(98-109)Online publication date: 14-Apr-2023
https://doi.org/10.1007/978-981-99-1642-9_9
Ferrari IFrisoni GItaliani PMoro GSartori C(2022)Comprehensive Analysis of Knowledge Graph Embedding Techniques Benchmarked on Link PredictionElectronics10.3390/electronics1123386611:23(3866)Online publication date: 23-Nov-2022
https://doi.org/10.3390/electronics11233866

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