Knowledge graph entity alignment networks with multi-information aggregation

Wenxuan Hu

doi:10.1117/12.2636404

6 May 2022 Knowledge graph entity alignment networks with multi-information aggregation

Wenxuan Hu

Proceedings Volume 12176, International Conference on Algorithms, Microchips and Network Applications; 121761R (2022) https://doi.org/10.1117/12.2636404
Event: International Conference on Algorithms, Microchips, and Network Applications 2022, 2022, Zhuhai, China

Abstract

Embedding-based methods have currently become the hit of the entity alignment, among which Graph Convolutional Networks model(GCNs) is receiving widespread attention because of its powerful capability of identifying isomorphic structures. Note that the aligned entities in real-world knowledge graphs always have non-isomorphic neighborhood structures, which may cause the GCN-based method to generate different representations for them. In addition, most embedding-based entity alignment methods ignore the relation information and semantic information of entities, which can cause inferior performance because of the lack of necessary information. This paper proposes a novel KG alignment framework named MGN(Multi-information aggregation Graph neural Networks), which aims at capturing the multiaspects features to learn the representations of entities at the same time. Our method can make up for the lacking of feature information and ease the non-isomorphism of structure effectively. The experiments on three real-world crosslingual datasets demonstrate that the MGN can get better results over the state-of-the-art alignment methods.

Citation Download Citation

Wenxuan Hu "Knowledge graph entity alignment networks with multi-information aggregation", Proc. SPIE 12176, International Conference on Algorithms, Microchips and Network Applications, 121761R (6 May 2022); https://doi.org/10.1117/12.2636404

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available