Matching Knowledge Graphs in Entity Embedding Spaces: An Experimental Study [Extended Abstract]-Reference-Cited by-同舟云学术

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Matching Knowledge Graphs in Entity Embedding Spaces: An Experimental Study [Extended Abstract]

Published:2024-05-13 Issue: Volume: Page:5741-5742
ISSN:
Container-title:2024 IEEE 40th International Conference on Data Engineering (ICDE)
language:
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Author:

Zeng Weixin¹,Zhao Xiang¹,Tan Zhen²,Tang Jiuyang¹,Cheng Xueqi³

Affiliation:

1. National University of Defense Technology,Laboratory for Big Data and Decision,China

2. National University of Defense Technology,Science and Technology on Information Systems Engineering Laboratory,China

3. Institute of Computing Technology, CAS,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10597630/10597390/10597800.pdf?arnumber=10597800

Reference10 articles.

1. Deep graph matching consensus;Fey;ICLR, 2020

2. ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch Similarities

3. From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment

4. A benchmarking study of embedding-based entity alignment for knowledge graphs

5. Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment

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