State-of-the-Art Instance Matching Methods for Knowledge Graphs?

Abstract

Instance matching has attracted a wide range of research attentions. A systematic literature review captures knowledge regarding the state-of-the-art systematically, to analyze and report it in the form of reusable knowledge. It is di cult to compare the performance of di erent instance matching methods, even when the same benchmarking dataset is used.

Introduction

Instance matching identi es instances from di erent data sources that refer to the same real-world entity [ 7 ]. It is di cult to identify the state-of-the-art solution since the every instance matching approach is tailored for speci c data and its properties. This study reviews the latest instance matching research. The approaches are often tested on di erent benchmarking datasets. Importantly, even when the same dataset is used, there is no single instance matching approach that performs well with every metric. Since some methods perform well on one subset and worse on the others, it is di cult to compare their performance. Future research can possibly explore ways for reducing human feedback while preserving its bene ts, for example by combining supervised and self-supervised approaches. ? Copyright © 2021 for this paper by its authors. Use permitted under Creative

Commons License Attribution 4.0 International (CC BY 4.0).

A. Boyko et al.

Acknowledgement

This work has been partially funded by the European Union's Horizon 2020 research and innovation programme, by the project of ARTICONF (825134), ENVRI-FAIR (824068) and BLUECLOUD (862409).

References

1. Fey , M. , Lenssen , J.E. , Morris , C. , Masci , J. , Kriege , N.M.: Deep graph matching consensus . ArXiv abs/ 2001 .09621 ( 2020 )

2. Liu , X. , Hong , H. , Wang , X. , Chen , Z. , Kharlamov , E. , Dong , Y. , Tang , J.: A selfsupervised method for entity alignment ( 2021 )

3. Tang , X. , Zhang , J., Chen , B. , Yang , Y. , Chen , H. , Li , C. : Bert-int: A bert-based interaction model for knowledge graph alignment . In: IJCAI . pp. 3174 { 3180 ( 2020 )

4. Yang , K. , Liu , S. , Zhao , J. , Wang , Y. , Xie , B. : Cotsae: Co-training of structure and attribute embeddings for entity alignment . In: AAAI ( 2020 )

5. Zeng , W. , Zhao , X. , Tang , J. , Li , X. , Luo , M. , Zheng , Q. : Towards entity alignment in the open world: An unsupervised approach . In: DASFAA ( 2021 )

6. Zeng , W. , Zhao , X. , Tang , J. , Lin , X. , Groth , P.T.: Reinforcement learning based collective entity alignment with adaptive features . ArXiv abs/2101 .01353 ( 2021 )

7. Zhu , Y. , Liu , H. , Wu , Z. , Du , Y. : Relation-aware neighborhood matching model for entity alignment . In: AAAI ( 2021 )