Article

Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned

Authors:

Max Berrendorf,

Evgeniy Faerman,

Valentyn Melnychuk,

Thomas SeidlAuthors Info & Claims

Advances in Information Retrieval: 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, April 14–17, 2020, Proceedings, Part II

Pages 3 - 11

https://doi.org/10.1007/978-3-030-45442-5_1

Published: 14 April 2020 Publication History

Abstract

In this work, we focus on the problem of entity alignment in Knowledge Graphs (KG) and we report on our experiences when applying a Graph Convolutional Network (GCN) based model for this task. Variants of GCN are used in multiple state-of-the-art approaches and therefore it is important to understand the specifics and limitations of GCN-based models. Despite serious efforts, we were not able to fully reproduce the results from the original paper and after a thorough audit of the code provided by authors, we concluded, that their implementation is different from the architecture described in the paper. In addition, several tricks are required to make the model work and some of them are not very intuitive.We provide an extensive ablation study to quantify the effects these tricks and changes of architecture have on final performance. Furthermore, we examine current evaluation approaches and systematize available benchmark datasets.We believe that people interested in KG matching might profit from our work, as well as novices entering the field. (Code: https://github.com/Valentyn1997/kg-alignment-lessons-learned).

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Cited By

Wang HLi JLuo T(2021)Graph Semantics Based Neighboring Attentional Entity Alignment for Knowledge GraphsIntelligent Computing Theories and Application10.1007/978-3-030-84529-2_30(355-367)Online publication date: 12-Aug-2021
https://dl.acm.org/doi/10.1007/978-3-030-84529-2_30
Berrendorf MWacker LFaerman E(2021)A Critical Assessment of State-of-the-Art in Entity AlignmentAdvances in Information Retrieval10.1007/978-3-030-72240-1_2(18-32)Online publication date: 28-Mar-2021
https://dl.acm.org/doi/10.1007/978-3-030-72240-1_2

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cover image Guide Proceedings

Advances in Information Retrieval: 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, April 14–17, 2020, Proceedings, Part II

Apr 2020

708 pages

ISBN:978-3-030-45441-8

DOI:10.1007/978-3-030-45442-5

Editors:
Joemon M. Jose
University of Glasgow, Glasgow, UK
,
Emine Yilmaz
University College London, London, UK
,
João Magalhães
Universidade NOVA de Lisboa, Lisbon, Portugal
,
Pablo Castells
Universidad Autónoma de Madrid, Madrid, Spain
,
Nicola Ferro
University of Padua, Padua, Italy
,
Mário J. Silva
Universidade de Lisboa, Lisbon, Portugal
,
Flávio Martins
Universidade NOVA de Lisboa, Lisbon, Portugal

© Springer Nature Switzerland AG 2020.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 14 April 2020

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Cited By

Wang HLi JLuo T(2021)Graph Semantics Based Neighboring Attentional Entity Alignment for Knowledge GraphsIntelligent Computing Theories and Application10.1007/978-3-030-84529-2_30(355-367)Online publication date: 12-Aug-2021
https://dl.acm.org/doi/10.1007/978-3-030-84529-2_30
Berrendorf MWacker LFaerman E(2021)A Critical Assessment of State-of-the-Art in Entity AlignmentAdvances in Information Retrieval10.1007/978-3-030-72240-1_2(18-32)Online publication date: 28-Mar-2021
https://dl.acm.org/doi/10.1007/978-3-030-72240-1_2

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